Recommendation System Research Papers - Academia.edu (original) (raw)

Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with each other by many means. Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer’s interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.

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- Collaborative Filtering, Content-Based Filtering, Association Rule, Recommendation System

Objective: Providing that data mining has been an effective solution of improving the efficiency and the effectiveness of the retail industry, this industry has been the subject of data mining science due to the nature of its data. In this study, the prediction of customer behavior in the retail industry of Fast Moving Consumer Goods is aimed at increasing the quantity and quality of sales in the study of Golpakhsh Avval Co. Methods: The present study is applied in terms of purpose, using data survey to collect data. The research is based on the CRISP-DM process, which uses the RFMCL clustering model, regression classification and regression techniques as well. Eventually, a collaborative recommendation method has been applied for recommendation. Results: The result is a forecasting model recommended to the best customers goods that they have not bought on a particular date and to a certain amount, so that, the order-based sale is changed to hot sale method. The final solution involves three sub models of customer clustering, sale forecasting and a recommendation system. The five variables model –with MSE/Range accuracy of 2.24% – is solved for recommendation of sales amount. Conclusion: By implementing the developed recommender system in Golpakhsh Avval Co., the proactive production master plan would be possible to execute. In addition, the marketing approach could be transformed from visiting sales to hot sales in the future which provides considerable savings in shipping and personnel costs.

- by Journal of Business Management
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- Sales forecasting, Recommendation System

Many fashion firms have enabled their business model to give extremely personalized experiences to their customers by using advanced CAD tools like CLO 3D, Marvelous-Designer, Browzwear, Lectra and many more for designing the garment and build a 3D avatar for the customized garment as well as web-based services to be integrated with the web and mobile-based applications. Due to the integration of highly advanced technologies for designing and giving personalized experience has increased the customer's expectations. In this paper, we have presented our initial work to build a garment fashion recommendation system for customized garments, which can be used with mobile and web applications. The proposed system structure is designed on the user's biometric profile and historical data of product order. We have collected the user's historical data from a fashion company dealing with customized made-to-measure garments. Proposed architecture for recommendation system is based on different data mining techniques like clustering, classification and association mining.

Slides of Ist lecture Mobile computing

- by Naseer Ahmad
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- Recommendation System

A code transaction modifies a set of code files and runs a set of tests to make sure that it does not break any of the existing features. But how do we choose the target set of tests? There could be 1000s of tests. Do we run all those tests? In that case, are we not wasting resources, and delaying the whole process of merging a code change? Can we choose a static set of tests? In that case, we have a chance of missing a set of tests which may be related to the code changes. How do we find out the optimal set of tests for a given code change-is the goal which we are trying to solve using the Recommendation System.

- by Ijariit Journal
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- Singular value decomposition, SVD, Recommendation System

This article investigates algorithms and their construction of cultural taste through a socio-technical analysis of the Netflix Recommender System. I examined the key algorithmic processes in the intersection of its technological infrastructure, cultural processes, and social relations by employing Taina Bucher’s three methodological tactics for ‘unknowing’ algorithms. Drawing from media logic and computational logic, I propose the concept of ‘algorithmic logics’ to define the assumptions, processes, and mechanisms that govern the construction of taste within the Netflix platform. I identified these four logics of taste – datafication, reconfiguration, interpellation, and reproduction – and argue that they reappropriate old apparatuses of social control and generate new capacities in engineering cultural processes. Together, these logics transform algorithms from procedural to self-generative machines in the guise of algorithmic objectivity, user agency, and post-demographic experiences. Algorithmic logics function as an ‘interpretative schema’ in making sense algorithms in their entanglement with social actors, institutions, and infrastructures.

- by Fatima Gaw
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- Algorithm, Mediatization (Communication Studies), Media Logic, Netflix

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a nondomain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborative via content recommendation system is presented.

- by Noha A Yousri
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- Semantic Analysis, Ontologies, Text Analysis, User needs

Recommendation Systems are changing from novelties which were used by a few E-commerce sites, to tools that are almost-shaping the world of E-commerce. Many of the largest commerce web sites are already using recommendation systems to encourage their customers, locate products. A recommendation system learns from a customer and recommends products that he/she will investigate most necessary from the available products. This research work is to recommend a product using Apriori algorithm. This algorithm is mainly used to find frequently purchased items/products. Its aim is to detect association rules.

Online social networking sites like, Facebook, Google and Twitter are suggested to share their public and personal information and make social relationship or connection with individual or people who can be even strangers. Existing social networking facilities recommends friends to users based on their social graphs, which might not be suitable to reflect a user's preferences on friend selection in their real life. In this system, human interest based friend recommendation system for social networks, which recommends friends to users based on his/her life styles instead of their social graphs and determine life styles of users from user-centric sensor data and measures the comparison of life styles between users and this scheme recommends friends to users if their way of lifestyle has high similarity. Social networking sites also include sharing of files or data among the users or group of users. Data sharing is not easier and an accurate analysis on the shared data provides more benefits to both the society and individuals. Data sharing with a large number of participants must take into account many issues, that is efficiency, data integrity and privacy of data owner. Also ranking is done based on searching of users profile information. Finally, this system also take part a feedback mechanism to improve the users satisfaction and recommendation accuracy.

- by Mohamed Samir
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- Computer Science, Semantic Analysis, Ontologies, Text Analysis

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

- by Jery Gao
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- Discourse Analysis, Software Engineering, Information Retrieval, Quality Management

A course recommender system has a great importance in expecting the selection of courses by students in an university, especially for new students who can't easily select the proper elective courses offered for a specific semester. The computer science department in Ajloun University College at Balqa Applied University (BAU) will be taken as a case study. In this paper, an efficient cluster based rule mining algorithm will be used on a course database to describe a courses recommendation system that assist students to choose elective courses based on students already studied these courses or some of them.

In this paper, we propose a movie genre recommendation system based on imbalanced survey data and unequal classification costs for small and medium-sized enterprises (SMEs) who need a data-based and analytical approach to stock favored movies and target marketing to young people. The dataset maintains a detailed personal profile as predictors including demographic, behavioral and preferences information for each user as well as imbalanced genre preferences. These predictors do not include movies' information such as actors or directors. The paper applies Gentle boost, Adaboost and Bagged tree ensembles as well as SVM machine learning algorithms to learn classification from one thousand observations and predict movie genre preferences with adjusted classification costs. The proposed recommendation system also selects important predictors to avoid overfitting and to shorten training time. This paper compares the test error among the above-mentioned algorithms that are used to recommend different movie genres. The prediction power is also indicated in a comparison of precision and recall with other state-of-the-art recommendation systems. The proposed movie genre recommendation system solves problems such as small dataset, imbalanced response, and unequal classification costs.

The necessity to improve performance of the processes within organizations, gave rise to many research that apply concepts from educational area in software development companies. Many studies are related to Organizational Learning (OL), an area that helps companies to improve their processes significantly through the reuse of experiences. In recent works, some approaches propose to generate courses in organizations from content produced by employees. The main limitation of these approaches is the high dependence of an expert, who is responsible by the courses. Even a qualified expert, can be unfamiliar with the real need of the team's learning, and mapping the organizational needs requires time and effort. This work presents a mechanism for software development companies, capable of recovery searches performed by employees on the internet, in order to discover the real necessity of the team's learning. From these needs is purposed a learning schema of a unit of learning (the structure of a course), so helping the expert in the course creation task. An initial experiment was conducted and the results indicate that the use of the approach is viable and may help an expert create units of learning, assisting to improve the OL in software development teams.

Online Health Communities (OHC) aim to support patients through offer them the opportunities to exchange support with others. However, patients have difficulties and problems locating expertise within the online health communities. In this regard, this study aims to create a patient recommender system to help users locate those with relevant experience and similar health status. Specifically, we aim to leverage the type of online social support users seek to determine the patient health status to build a patient status prediction model. Building the model will help create a peer recommendation system for online support group members to easily locate peers and build a sustainable online health community. Building this type of recommendation system will help patients to effectively interact with other patients who have same health status. Moreover, it will help online health communities in improving the services provided, which in turn will be reflected positively on patient's health status.

- by Cherie Noteboom
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- Sociology, Software Engineering, Support groups, Online Health Communities

Bio-inspired design was introduced as an alternative method to encourage breakthrough innovations during design projects by stimulating analogical reasoning and thinking of designers. However, the method did not perform as well as researchers expected because most designers, who are novices in the fields of biology and ecology, cannot infer the proper analogue (i.e. biological system) from nature. To resolve this fundamental problem, a causal model based representation framework for 'analogical reasoning' – searching and selecting the biological systems to apply – have been developed. In addition, ontology based repository structures and retrieval systems have been proposed to support 'analogical thinking' of designers. Nevertheless, these systematic approaches still restrict the candidates and inevitably lose potential biological systems relevant to the design project, due to the 'physical relation' biased problem and the ambiguity of the indexing mechanism of both current representation frameworks and retrieval systems. For example, the causality based support system known as a robust representation framework for a single biological system, stores information of a biological system only by its internal 'physical relations' and retrieves biological systetabms only by the physical relevance. However, from the perspective of ecological thinking, the further relatedness of 'physical, biological, and ecological relations' composes the holistic concept used to identify an organism in the flow of evolution because the 'biological and ecological relations' are also involved in the traits that designers may be interested in. Therefore, the supplementary information for 'biological and ecological relations' must be added to index the biological and environmental interactions, and to use the connectivity among entire organisms in the retrieval process. In this research, a causality based holistic representation framework for biological systems and an 'all-connected' ontology based repository and retrieval system are developed as a knowledge-based recommendation system to support bio-inspired design. The knowledge-based system we developed allows engineering designers to search and select a particular biological system and extract design strategy without much biological knowledge. This effort provides more opportunities in a bio-inspired design process by adding potential biological systems that might previously not have been considered.

- by Ji-Hyun Lee
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- Ontology, Knowledge-Based Systems, Bio inspired design, Recommendation System

The biggest feature and advantage of smart phone is possibility of storing and utilizing many features as applications, Applications market is also infinitely expandable. Utilization of applications downloaded by the user, however, is significantly low compared to the built-in applications.
For fully taking advantage of these feature-rich applications, we need a recommendation system which can help to increase the usability. In this study, I introduce the concept of socially aware computing system adapted for more specific context awareness. It makes possible that analysis of the social behavior of the users increases the complexity of the context and can be close to real-life reasoning. In other word, it is possible to get the context closer to reality by combining social behavior analysis of the user.
Through this reasoning process, I proposed two alternatives of context-ware application recommend model and try to increase the utilization of smart phone application.
In this study, I examined scalability of the semantic context-awareness system combined with socially aware computing for informationizing of the dynamic real world. I also propose the organic user interface model increasing utilization of smart phone applications by using those context information.

- by Ji-Hyun Lee
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- Social Networking, User Interface, Context Awareness, Recommendation System

Recommendation systems help online users with advantageous access to the items and services they may be intrested on this present reality. Because of the requirements of compelling forecast and productive recommendation, it is advantageous for the location-based services (LBS), to discover the user's next location that the user may visit. So in this paper, diverse kinds of methodologies used to discover, anticipate, and examine location based services are talked about. It is important to convey those expectation and recommendation services for ongoing real time application with direction mapping. While considering location information's, at that point the information measure ended up noticeably colossal and dynamic. Finding ideal answer for anticipate the rating in view of the location and unequivocal conduct is overviewed.

- by IJFRCSCE Journal
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- Location Based Services, Data Mining, Ratings, Big Data

"Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a non- domain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborative via content recommendation system is presented."

- by Ibrahim Sabek
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- Ontologies, Recommendation System, Semantic-based Analysis

Recommender systems are systems that filter information and suggest items based on user profiles or preferences. As recommender systems are increasingly incorporated in our daily lives and users become more dependent upon these systems during their decision-making process in their daily life,researchers and developers are continuously looking for better approaches to improve the performance of recommender systems and alleviate existing problems in the systems. The cold-start is a common problem in recommendation systems. This work introduces a book recommendation website, which employs content-based and popularity-based techniques based on Google Trends. The recommendation algorithms used in the system are compared and their performance in cold-start scenarios are evaluated. It is found that content-based recommendation performs with lower precision and recall with higher personalization and coverage whereas popularity recommendation performs with higher precision and recall with low personalization and coverage. The results also show that recommendation algorithms implementing Google Trends are able to perform with higher precision and recall with a lower personalization score

- by IAEME Publication
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- Content-based instruction, Filtering, Recommendation System, Iaeme Ijaret

Recommender systems are systems that filter information and suggest items based on user profiles or preferences. As recommender systems are increasingly incorporated in our daily lives and users become more dependent upon these systems during their decision-making process in their daily life, researchers and developers are continuously looking for better approaches to improve the performance of recommender systems and alleviate existing problems in the systems. The cold-start is a common problem in recommendation systems. This work introduces a book recommendation website, which employs content-based and popularity-based techniques based on Google Trends. The recommendation algorithms used in the system are compared and their performance in cold-start scenarios are evaluated. It is found that content-based recommendation performs with lower precision and recall with higher personalization and coverage whereas popularity recommendation performs with higher precision and recall with low personalization and coverage. The results also show that recommendation algorithms implementing Google Trends are able to perform with higher precision and recall with a lower personalization score

- by IAEME Publication
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- Content-based instruction, Filtering, Recommendation System

Die Studie „Empfehlungen in Krisenzeiten“ untersucht, inwiefern durch YouTubes Empfehlungsalgorithmen desinformative Inhalte auf der Plattform befördert werden und inwieweit verlässliche und vielfältige Informationsangebote dabei sichtbar... more

- by Joachim Allgaier
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- Algorithms, Online Video, Youtube, Recommendation

An online recommendation system (RS) involves using information technology and customer information to tailor electronic commerce interactions between a business and individual customers. Extant information systems (IS) studies on RS have approached the phenomenon from many different perspectives, and our understanding of the nature and impacts of RS is fragmented. The current study reviews and synthesizes extant empirical IS studies to provide a coherent view of research on RS and identify gaps and future directions. Specifically, we review 40 empirical studies of RS published in 31 IS journals and five IS conference proceedings between 1990 and 2013. Using a recommendation process theoretical framework, we categorize these studies in three major areas addressed by RS research: understanding consumers, delivering recommendations, and the impacts of RS. We review and synthesize the extant literature in each area and across areas. Based on the review and synthesis, we surface research gaps and provide suggestions and potential directions for future research on recommendation systems.

- by Elena Karahanna
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- Information Systems, Computer Science, Recommender Systems, Personalization

Recommendation as a social process plays an important role in many applications as WWW has created the universe as a global village, with an explosive growth of enormous information. The paper presents an overview of the field of recommender systems along with the description of various approaches that are being used for generating recommendations. Recommendation techniques can be classified in to three major categories: Collaborative Filtering, Content Based and Hybrid Recommendations. The paper elaborates these approaches and discusses their limitations by describing the major problems suffered by recommendation methods. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for recommendation techniques, which can be served as a roadmap for research and practice in this area.

- by sandeep mann
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- Engineering, Computer Science, Collaborative Filtering, Recommender System
- by abdullah wahbeh
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- Sociology, Software Engineering, Support groups, Online Health Communities

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a non-domain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborativ...

- by محمد سمير
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- Semantic Analysis, Ontologies, Text Analysis, User needs

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a non-domain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborativ...

- by Moustafa Badawy
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- Semantic Analysis, Ontologies, Text Analysis, User needs

Buleleng Regency has many unique cultures and beauty of natural sceneries which are always the charm and attraction for tourists. It requires the effort to increase the number of visits of the tourists by providing the information of tour object in accordance with the tourists' interest and also easily for accessible. This study aimed at designing a mobile-based CBR system using Dempster-Shafer modification rule to provide the recommendation of tourist spots in Buleleng Regency. The process of recommendation based on the data tourist trips in the past as represented into the case based. The data contained in the case-based consisted of a traveler profile and tourist spots that are visited. Traveler profile included gender, country, age, occupation, income per month, and the frequency of visits. The results of this study indicated that the mobile-based CBR system using the Dempster-Shafer modification rule can be applied in providing recommendations of tourist spots for tourists who visited Buleleng Regency. Based on tests performed using K-Fold Cross Validation, were showed that the accuracy average of recommendations of tourist spots was 1) 18% for fully accordance, 2) 62% for partial accordance and 3) 18% of error rate.

- by kadek wirawan
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- Dempster-Shafer Analysis, Case Based Reasoning, Mobile phone, Computer Applications

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a non-domain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborative via content recommendation system is presented.

- by moustafa badawy
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- Semantic Analysis, Ontologies, Text Analysis, User needs

- by kadek Wirawan
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- Dempster-Shafer Analysis, Case Based Reasoning, Mobile phone, Recommendation System