Study of Recommender Systems Techniques (original) (raw)
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Hybrid recommender systems: Survey and experiments
User Modeling and User-Adapted Interaction, 2002
Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.
A COMPARATIVE STUDY ON RECOMMENDATION SYSTEM USING HYBRID APPROACH
Recommendation is a wide used technique to guide user to settle on the proper product on-line because it becomes the essential feature for a better E-commerce. Many recommender applications use item to item collaborative approach, i.e., overlapping previous user's data with new user's data and recommending remaining items. An item can be recommended to the user based on user's interests, favorite subjects, occupation of the customer, person's gender, age, etc. This literature survey tries to explain about item to item collaborative approach along with new features which improves the performance of the system. Not only in E-commerce, in many web applications recommendations play an important role such as book recommendation for a library, research paper recommendation based on previous searches while searching for a paper, dynamic recommendation system using web data mining and on social websites.
Design and Development of Decision Support Based Hybrid Recommender System
2018
Increasing recommendation diversity: i.e. ensuring that the user does not get repeated recommendation of similar items. Aside from these concrete goals, a number of soft goals like providing overall user satisfaction, insights into the needs of the user, and help customize the user experience further, are also met by the recommender system. 1.1 Approaches of Recommender Systems Most recommender systems take either of two basic approaches: Collaborative Filtering Collaborative filtering is a method of making automatic predictions (or filtering) about the interests of a user by collecting preferences from many users. Collaborative filtering approach aggregates ratings, recognizes similarities between individuals, and generates recommendations based on inter-user comparisons (Ekstrand, Riedl & Konstan, 2011). It is assumed that the individual ratings represent fairly constant opinions which can be analyzed to provide a reasonable estimate of the actual individual preferences. The main advantages of the approach are that it is simple as it focuses on only the item rated highly by peers that reduce workload, and it can be applied to almost any type of content. Content based filtering Content-based filtering methods are based on description of the items and a profile of the user's preferences. Keywords are used to describe the items. A user profile is then built to indicate the
Overview of Recommendation System: Approaches and their Prosperity
International Journal for Research in Applied Science and Engineering Technology, 2020
The information on the internet is tremendous to retrieve the required data from a vast amount of information available on the internet is strenuous. To make it easier we have a recommendation system or engine. These are chiefly used in commercial applications. This system filters the information dynamically based on user's interests and preferences. It has an essential feature to predict whether an individual user would prefer items or not based on the user's predilection. The recommender system plays a vital role in a variety of areas like product and service-based companies. Web recommender systems are categorized into various approaches such as collaborative filtering, content based, knowledge based and hybrid recommender systems. Many recommender systems are used by some of the popular websites like Amazon.com, Netflix.com etc. This paper focuses on foremost challenges faced by recommender systems and their solutions. Our findings indicate that the use of a hybrid approach is better than other individual approaches. We conclude that the recommender system increases the value and economy to the company by simply satisfying the customer needs and interests.
Emerging Technologies and Applications for Searching the Web Effectively
Recommendation systems have been used in e-commerce sites to make product recommendations and to provide customers with information that helps them decide which product to buy. They are based on different methods and techniques for suggesting products with the most well known being collaborative and content-based filtering. Recently, several recommendation systems adopted hybrid approaches by combining collaborative and content-based features as well as other techniques in order to avoid their limitations. In this chapter, we investigate hybrid recommendations systems and especially the way they support movie e-shops in their attempt to suggest movies to customers. Specifically, we introduce an approach where the knowledge about customers and movies is extracted from usage mining and ontological data in conjunction with customer-movie ratings and matching techniques between customers. This integration provides additional knowledge about customers’ preferences and allows the producti...
Recommender systems: models, challenges and opportunities, 2023
The purpose of this study is to provide a comprehensive overview of the latest developments in the field of recommender systems. In order to provide an overview of the current state of affairs in this sector and highlight the latest developments in recommender systems, the research papers available in this area were analyzed. The place of recommender systems in the modern world was defined, their relevance and role in people's daily lives in the modern information environment were highlighted. The advantages of recommender systems and their main properties are considered. In order to formally define the concept of recommender systems, a general scheme of recommender systems was provided and a formal task was formulated. A review of different types of recommender systems is carried out. It has been determined that personalized recommender systems can be divided into content filtering-based systems, collaborative filtering-based systems, and hybrid recommender systems. For each type of system, the author defines them and reviews the latest relevant research papers on a particular type of recommender system. The challenges faced by modern recommender systems are separately considered. It is determined that such challenges include the issue of robustness of recommender systems (the ability of the system to withstand various attacks), the issue of data bias (a set of various data factors that lead to a decrease in the effectiveness of the recommender system), and the issue of fairness, which is related to discrimination against users of recommender systems. Overall, this study not only provides a comprehensive explanation of recommender systems, but also provides information to a large number of researchers interested in recommender systems. This goal was achieved by analyzing a wide range of technologies and trends in the service sector, which are areas where recommender systems are used.
Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System
Recommender systems or recommendation systems are a subset of information filtering system that used to anticipate the 'evaluation' or 'preference' that user would feed to an item. In recent years E-commerce applications are widely using Recommender system. Generally the most popular Ecommerce sites are probably music, news, books, research articles, and products. Recommender systems are also available for business experts, jokes, restaurants, financial services, life insurance and twitter followers. Recommender systems have formulated in parallel with the web. Initially Recommender systems were based on demographic, content-based filtering and collaborative filtering. Currently, these systems are incorporating social information for enhancing a quality of recommendation process. For betterment of recommendation process in the future, Recommender systems will use personal, implicit and local information from the Internet. This paper provides an overview of recommender systems that include collaborative filtering, content-based filtering and hybrid approach of recommender system.
A Comparing Collaborative Filtering and Hybrid Recommender System for E-Commerce
IJRASET, 2021
Here we are building an collaborative filtering matrix factorization based hybrid recommender system to recommend movies to users based on the sentiment generated from twitter tweets and other vectors generated by the user in their previous activities. To calculate sentiment data has been collected from twitter using developer APIs and scrapping techniques later these are cleaned, stemming, lemetized and generated sentiment values. These values are merged with the movie data taken and create the main data frame.The traditional approaches like collaborative filtering and content-based filtering have limitations like it requires previous user activities for performing recommendations. To reduce this dependency hybrid is used which combines both collaborative and content based filtering techniques with the sentiment generated above.
A Survey on Hybrid Recommendation Systems
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021
As India is moving fast towards digital economy, E-commerce industry has been on rise. Many platforms such as Amazon and Flipkart provide their customers with a shopping experience better than actual physical stores. Several E-commerce websites use different methods to improve the customer engagement and revenue. One such technique is the use of personalized recommendation systems which uses customer’s data like interests, purchase history, ratings to suggest new products which they may like. Recommendation systems are used by E-commerce websites to suggest new products to their users. The products can be suggested based on the top merchants on the website, based on the interests of the user or based the past purchase pattern of the customer. Recommender systems are machine learning based systems that help users discover new products. Due to the recent pandemic situation of 2020 and 2021, many of the local retail stores have been trying to shift their business to online platforms such as dedicated websites or social media. The proposed methodology based on Machine Learning aims to enable local online retail business owners to enhance their customer engagement and revenue by providing users with personalized recommendations using past data using methods such as Collaborative Filtering and Content-Based Filtering.
Hybrid Recommender Systems: A Systematic Literature Review
2017
Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc.