Joining Collaborative and Content-based Filtering (original) (raw)
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Unifying collaborative and content-based filtering
Proceedings of the Twenty First International Conference on Machine Learning, 2004
Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a novel, unified approach that systematically integrates all available training information such as past user-item ratings as well as attributes of items or users to learn a prediction function. The key ingredient of our method is the design of a suitable kernel or similarity function between user-item pairs that allows simultaneous generalization across the user and item dimensions. We propose an on-line algorithm (JRank) that generalizes perceptron learning. Experimental results on the EachMovie data set show significant improvements over standard approaches.
A Graph-Based Method for Combining Collaborative and Content-Based Filtering
Lecture Notes in Computer Science, 2008
Collaborative filtering and content-based filtering are two main approaches to make recommendations in recommender systems. While each approach has its own strengths and weaknesses, combining the two approaches can improve recommendation accuracy. In this paper, we present a graph-based method that allows combining content information and rating information in a natural way. The proposed method uses user ratings and content descriptions to infer user-content links, and then provides recommendations by exploiting these new links in combination with user-item links. We present experimental results showing that the proposed method performs better than a pure collaborative filtering, a pure content-based filtering, and a hybrid method.
A Framework for Collaborative, Content-Based and Demographic Filtering
A Framework for Collaborative, Content-Based and Demographic Filtering
We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the ratings of the user on other pages and the contents of these pages, the ratings given to that page by other users and the ratings of these other users on other pages and demographic information about users. We describe how each type of information may be used individually and then discuss an approach to combining recommendations from multiple sources. We illustrate each approach and the combined approach in the context of recommending restaurants.
2014
Content-based and collaborative filtering methods are the most successful solutions in recommender systems. Content based method is based on items attributes. This method checks the features of users favourite items and then proposes the items which have the most similar characteristics with those items. Collaborative filtering method is based on the determination of similar items or similar users, which are called item-based and user-based collaborative filtering, respectively.In this paper we propose a hybrid method that integrates collaborative filtering and content-based methods. The proposed method can be viewed as user-based Collaborative filtering technique. However to find users with similar taste with active user, we used content features of the item under investigation to put more emphasis on users rating for similar items. In other words two users are similar if their ratings are similar on items that have similar context. This is achieved by assigning a weight to each ra...
Using Content Features to Enhance the Performance of User-Basedcollaborative Filtering
International Journal of Artificial Intelligence & Applications, 2014
Content-based and collaborative filtering methods are the most successful solutions in recommender systems. Content-based method is based on item's attributes. This method checks the features of user's favourite items and then proposes the items which have the most similar characteristics with those items. Collaborative filtering method is based on the determination of similar items or similar users, which are called item-based and user-based collaborative filtering, respectively.In this paper we propose a hybrid method that integrates collaborative filtering and content-based methods. The proposed method can be viewed as user-based Collaborative filtering technique. However to find users with similar taste with active user, we used content features of the item under investigation to put more emphasis on user's rating for similar items. In other words two users are similar if their ratings are similar on items that have similar context. This is achieved by assigning a weight to each rating when calculating the similarity of two users.We used movielens data set to access the performance of the proposed method in comparison with basic user-based collaborative filtering and other popular methods.
Collaborative Filtering Based on Bi-Relational Data Representation
Foundations of Computing and Decision Sciences, 2013
Widely-referenced approaches to collaborative filtering (CF) are based on the use of an input matrix that represents each user profile as a vector in a space of items and each item as a vector in a space of users. When the behavioral input data have the form of (userX, likes, itemY) and (userX, dislikes, itemY) triples one has to propose a representation of the user feedback data that is more suitable for the use of propositional data than the ordinary user-item ratings matrix. We propose to use an element-fact matrix, in which columns represent RDF-like behavioral data triples and rows represent users, items, and relations. By following such a triple-based approach to the bi-relational behavioral data representation we are able to improve the quality of collaborative filtering. One of the key findings of the research presented in this paper is that the proposed bi-relational behavioral data representation, while combined with reflective matrix processing, significantly outperforms state-of-the-art collaborative filtering methods based on the use of a 'standard' user-item matrix.
Hike the Performance of Collaborative Filtering Algorithm with the Inclusion of Multiple Attributes
International Journal of Information Technology and Computer Science, 2018
At a recent time, digital data increases very speedily from small business to large business. In this span of internet explosion, choices are also increases and it makes the selection of products very difficult for users so it demands some recommendation system which provides good and meaningful suggestions to users to help them to purchase or select products of their own choice and get benefited. Collaborative filtering technique works very productive to provide personalized suggestions. It works based on the past given ratings, behavior and choices of users to provide recommendations. To boost its performance many other algorithms and techniques can be combined with it. This paper describes the method to boost the performance of collaborative filtering algorithm by taking multiple attributes in consideration where each attribute has some weight.
Structured Collaborative Filtering
ir.ii.uam.es
In a general collaborative filtering (CF) setting, a user profile contains a set of previously rated items and is used to represent the user's interest. Unfortunately, most CF approaches ignore the underlying structure of user profiles. In this paper, we argue that a certain class of interest is best represented jointly by several items, drawing an analogy to "phrases" in text retrieval, which are not equivalent to the separate meaning of their words. At an alternative stance, we also consider the situation where, analogously to word synonyms, two items might be substitutable when representing a class of interest. We propose an approach integrating these two notions as opposing poles on a continuum spectrum. Upon this, we model the underlying structure in user profiles, drawing an analogy with text retrieval. The approach gives rise to a novel structured Vector Space Model for CF. We show that item-based CF approaches are a special case of the proposed method.