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Rong Jin - One of the best experts on this subject based on the ideXlab platform.
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A study of mixture models for Collaborative Filtering
Information Retrieval, 2006Co-Authors: Rong Jin, Chengxiang ZhaiAbstract:Collaborative Filtering is a general technique for exploiting the preference patterns of a group of users to predict the utility of items for a particular user. Three different components need to be modeled in a Collaborative Filtering problem: users, items, and ratings. Previous research on applying probabilistic models to Collaborative Filtering has shown promising results. However, there is a lack of systematic studies of different ways to model each of the three components and their interactions. In this paper, we conduct a broad and systematic study on different mixture models for Collaborative Filtering. We discuss general issues related to using a mixture model for Collaborative Filtering, and propose three properties that a graphical model is expected to satisfy. Using these properties, we thoroughly examine five different mixture models, including Bayesian Clustering (BC), Aspect Model (AM), Flexible Mixture Model (FMM), Joint Mixture Model (JMM), and the Decoupled Model (DM). We compare these models both analytically and experimentally. Experiments over two datasets of movie ratings under different configurations show that in general, whether a model satisfies the proposed properties tends to be correlated with its performance. In particular, the Decoupled Model, which satisfies all the three desired properties, outperforms the other mixture models as well as many other existing approaches for Collaborative Filtering. Our study shows that graphical models are powerful tools for modeling Collaborative Filtering, but careful design is necessary to achieve good performance.
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flexible mixture model for Collaborative Filtering
International Conference on Machine Learning, 2003Co-Authors: Rong JinAbstract:This paper presents a flexible mixture model (FMM) for Collaborative Filtering. FMM extends existing partitioning/clustering algorithms for Collaborative Filtering by clustering both users and items together simultaneously without assuming that each user and item should only belong to a single cluster. Furthermore, with the introduction of 'preference' nodes, the proposed framework is able to explicitly model how users rate items, which can vary dramatically, even among the users with similar tastes on items. Empirical study over two datasets of movie ratings has shown that our new algorithm outperforms five other Collaborative Filtering algorithms substantially.
Jiming Liu - One of the best experts on this subject based on the ideXlab platform.
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Social Collaborative Filtering by Trust
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017Co-Authors: Bo Yang, Yu Lei, Jiming LiuAbstract:Recommender systems are used to accurately and actively provide users with potentially interesting information or services. Collaborative Filtering is a widely adopted approach to recommendation, but sparse data and cold-start users are often barriers to providing high quality recommendations. To address such issues, we propose a novel method that works to improve the performance of Collaborative Filtering recommendations by integrating sparse rating data given by users and sparse social trust network among these same users. This is a model-based method that adopts matrix factorization technique that maps users into low-dimensional latent feature spaces in terms of their trust relationship, and aims to more accurately reflect the users reciprocal influence on the formation of their own opinions and to learn better preferential patterns of users for high-quality recommendations. We use four large-scale datasets to show that the proposed method performs much better, especially for cold start users, than state-of-the-art recommendation algorithms for social Collaborative Filtering based on trust.
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social Collaborative Filtering by trust
International Joint Conference on Artificial Intelligence, 2013Co-Authors: Bo Yang, Yu Lei, Dayou Liu, Jiming LiuAbstract:To accurately and actively provide users with their potentially interested information or services is the main task of a recommender system. Collaborative Filtering is one of the most widely adopted recommender algorithms, whereas it is suffering the issues of data sparsity and cold start that will severely degrade quality of recommendations. To address such issues, this article proposes a novel method, trying to improve the performance of Collaborative Filtering recommendation by means of elaborately integrating twofold sparse information, the conventional rating data given by users and the social trust network among the same users. It is a model-based method adopting matrix factorization technique to map users into low-dimensional latent feature spaces in terms of their trust relationship, aiming to reflect users' reciprocal influence on their own opinions more reasonably. The validations against a real-world dataset show that the proposed method performs much better than state-of-the-art recommendation algorithms for social Collaborative Filtering by trust.
Yi Ding - One of the best experts on this subject based on the ideXlab platform.
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EFFECTIVE AND EFFICIENT Collaborative Filtering
2011Co-Authors: Yi DingAbstract:Collaborative Filtering is regarded as one of the most promising approaches in recommender systems. To date, it is best known for its use on e-commerce web sites. It has also been widely used in other areas, for example, Filtering Usenet News, recommending TV shows and web personalization. However, a survey of existing algorithms shows there remain some fundamental research questions in overcoming some challenges for Collaborative Filtering system. These questions influence the prevalence of the recommendation systems to a great extent. • Scalability: A large online retailer might have huge amounts of data, tens of millions of customers and millions of distinct catalogue items. These “long user rows” slow down the performance of the recommender system, further reducing scalability. • Accuracy: Accuracy of Collaborative Filtering considers the problem of how the system would successfully measure the similarity between users (in user-based approaches) or items (in item-based approaches) in order to discover the intrinsic properties that exist amongst users and/or amongst item. Users need recommendations of high quality and they can trust to help them. • Robustness: Robustness of Collaborative Filtering systems is defined as the ability to provide accurate predictions given some degree of noise in the data. • Sparsity: It refers to the fact that most users do not rate many items and hence the user-item rating matrix is very sparse and insufficient to identify similarities in consumer interests. In many commercial recommender systems, even active users may have purchased less than 1% of the items, (1% of 2 million books is 20,000 books). As a result, the accuracy of recommendations may be poor. • Cold Start: It has been used to describe the situation when almost nothing is known about the new users or new items. This dissertation focuses on effectiveness and efficiency of Collaborative Filtering technologies. We proposed a novel Collaborative Filtering framework which is capable of dealing with an immense and dynamic dataset effectively and efficiently. Specifically, we improved the existing algorithms from the following aspects: 1. Generic Model: Integrate the content-based Filtering and Collaborative Filtering by unifying the external attributes of users and items with rating information in a generic model. 2. Effectiveness: 1)Propose a new method of computing similarity in Collaborative Filtering to better reflect the reality. 2)Consider the interest drift in Collaborative Filtering. We introduce the time recency to tackle it. 3. Efficiency: Propose a number of solutions towards traditional item-based and user-based Collaborative Filtering which can handle a large scale of data in the dynamical environment. The experiments have shown that our proposed framework can substantially improve the performance of traditional Collaborative Filtering algorithms. The main contributions of this dissertation include: demonstrating a generic model, improving the accuracy of Collaborative Filtering through the new similarity computation and addressing interest drift in the traditional Collaborative Filtering and providing a highly efficient incremental framework which can be easily used for the online applications and an effective indexing which can reduce the time complexity of the traditional algorithms dramatically.
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Recency-based Collaborative Filtering
2006Co-Authors: Yi Ding, Maria E. OrlowskaAbstract:Collaborative Filtering is regarded as one of the most promising recommendation algorithms. Traditional approaches for Collaborative Filtering do not take concept drift into account. For example, user purchase interests may be volatile. A new mother may be interested in baby toys, although previously she had no interest in these. A man may like romantic films while he preferred action movies one year ago. Collaborative Filtering is characterized by concept drift in the real world. To make time-critical predictions, we argue that the target users' recent ratings re°ect his/her future preferences more than older ratings. In this paper, we present a novel algorithm namely recencybased Collaborative Filtering to explore the weights for items based on their expected accuracy on the future preferences. Our proposed approach is based on itembased Collaborative Filtering algorithms. Specifically, we design a new similarity function to produce similarity scores that better re°ect the reality. Our experimental results have shown that the new algorithm substantially improves the precision of traditional Collaborative Filtering algorithms.
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ADC - Recency-based Collaborative Filtering
2006Co-Authors: Yi Ding, Maria E. OrlowskaAbstract:Collaborative Filtering is regarded as one of the most promising recommendation algorithms. Traditional approaches for Collaborative Filtering do not take concept drift into account. For example, user purchase interests may be volatile. A new mother may be interested in baby toys, although previously she had no interest in these. A man may like romantic films while he preferred action movies one year ago. Collaborative Filtering is characterized by concept drift in the real world. To make time-critical predictions, we argue that the target users' recent ratings reflect his/her future preferences more than older ratings. In this paper, we present a novel algorithm namely recency-based Collaborative Filtering to explore the weights for items based on their expected accuracy on the future preferences. Our proposed approach is based on item-based Collaborative Filtering algorithms. Specifically, we design a new similarity function to produce similarity scores that better reflect the reality. Our experimental results have shown that the new algorithm substantially improves the precision of traditional Collaborative Filtering algorithms.
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time weight Collaborative Filtering
Conference on Information and Knowledge Management, 2005Co-Authors: Yi DingAbstract:Collaborative Filtering is regarded as one of the most promising recommendation algorithms. The item-based approaches for Collaborative Filtering identify the similarity between two items by comparing users' ratings on them. In these approaches, ratings produced at different times are weighted equally. That is to say, changes in user purchase interest are not taken into consideration. For example, an item that was rated recently by a user should have a bigger impact on the prediction of future user behaviour than an item that was rated a long time ago. In this paper, we present a novel algorithm to compute the time weights for different items in a manner that will assign a decreasing weight to old data. More specifically, the users' purchase habits vary. Even the same user has quite different attitudes towards different items. Our proposed algorithm uses clustering to discriminate between different kinds of items. To each item cluster, we trace each user's purchase interest change and introduce a personalized decay factor according to the user own purchase behaviour. Empirical studies have shown that our new algorithm substantially improves the precision of item-based Collaborative Filtering without introducing higher order computational complexity.
Bo Yang - One of the best experts on this subject based on the ideXlab platform.
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Social Collaborative Filtering by Trust
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017Co-Authors: Bo Yang, Yu Lei, Jiming LiuAbstract:Recommender systems are used to accurately and actively provide users with potentially interesting information or services. Collaborative Filtering is a widely adopted approach to recommendation, but sparse data and cold-start users are often barriers to providing high quality recommendations. To address such issues, we propose a novel method that works to improve the performance of Collaborative Filtering recommendations by integrating sparse rating data given by users and sparse social trust network among these same users. This is a model-based method that adopts matrix factorization technique that maps users into low-dimensional latent feature spaces in terms of their trust relationship, and aims to more accurately reflect the users reciprocal influence on the formation of their own opinions and to learn better preferential patterns of users for high-quality recommendations. We use four large-scale datasets to show that the proposed method performs much better, especially for cold start users, than state-of-the-art recommendation algorithms for social Collaborative Filtering based on trust.
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social Collaborative Filtering by trust
International Joint Conference on Artificial Intelligence, 2013Co-Authors: Bo Yang, Yu Lei, Dayou Liu, Jiming LiuAbstract:To accurately and actively provide users with their potentially interested information or services is the main task of a recommender system. Collaborative Filtering is one of the most widely adopted recommender algorithms, whereas it is suffering the issues of data sparsity and cold start that will severely degrade quality of recommendations. To address such issues, this article proposes a novel method, trying to improve the performance of Collaborative Filtering recommendation by means of elaborately integrating twofold sparse information, the conventional rating data given by users and the social trust network among the same users. It is a model-based method adopting matrix factorization technique to map users into low-dimensional latent feature spaces in terms of their trust relationship, aiming to reflect users' reciprocal influence on their own opinions more reasonably. The validations against a real-world dataset show that the proposed method performs much better than state-of-the-art recommendation algorithms for social Collaborative Filtering by trust.
Xiang Yan - One of the best experts on this subject based on the ideXlab platform.
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Manipulation Robustness of Collaborative Filtering
Management Science, 2010Co-Authors: Benjamin Van Roy, Xiang YanAbstract:A Collaborative Filtering system recommends to users products that similar users like. Collaborative Filtering systems influence purchase decisions and hence have become targets of manipulation by unscrupulous vendors. We demonstrate that nearest neighbors algorithms, which are widely used in commercial systems, are highly susceptible to manipulation and introduce new Collaborative Filtering algorithms that are relatively robust.
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RecSys - Manipulation-resistant Collaborative Filtering systems
Proceedings of the third ACM conference on Recommender systems - RecSys '09, 2009Co-Authors: Benjamin Van Roy, Xiang YanAbstract:A Collaborative Filtering system recommends to users products that similar users like. Collaborative Filtering systems influence purchase decisions, and hence have become targets of manipulation by unscrupulous vendors. We provide theoretical and empirical results demonstrating that while common nearest neighbor algorithms, which are widely used in commercial systems, can be highly susceptible to manipulation, a class of Collaborative Filtering algorithms which we refer to as linear is relatively robust. These results provide guidance for the design of future Collaborative Filtering systems.
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Manipulation robustness of Collaborative Filtering systems
SSRN Electronic Journal, 2009Co-Authors: Ashish Goel, Xiang YanAbstract:A Collaborative Filtering system recommends to users products that similar users like. Collaborative Filtering systems influence purchase decisions, and hence have become targets of manipulation by unscrupulous vendors. We provide theoretical and empirical results demonstrating that while common nearest neighbor algorithms, which are widely used in commercial systems, can be highly susceptible to manipulation, two classes of Collaborative Filtering algorithms which we refer to as linear and asymptotically linear are relatively robust. These results provide guidance for the design of future Collaborative Filtering systems.
