54,753 research outputs found
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and
personalised information over massive amounts of data available. We propose an
unified framework called Preference Network (PN) that jointly models various
types of domain knowledge for the task of recommendation. The PN is a
probabilistic model that systematically combines both content-based filtering
and collaborative filtering into a single conditional Markov random field. Once
estimated, it serves as a probabilistic database that supports various useful
queries such as rating prediction and top- recommendation. To handle the
challenging problem of learning large networks of users and items, we employ a
simple but effective pseudo-likelihood with regularisation. Experiments on the
movie rating data demonstrate the merits of the PN.Comment: In Proc. of 6th Australasian Data Mining Conference (AusDM), Gold
Coast, Australia, pages 195--202, 200
Trust-Networks in Recommender Systems
Similarity-based recommender systems suffer from significant limitations, such as data sparseness and scalability. The goal of this research is to improve recommender systems by incorporating the social concepts of trust and reputation. By introducing a trust model we can improve the quality and accuracy of the recommended items. Three trust-based recommendation strategies are presented and evaluated against the popular MovieLens [8] dataset
Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
Following recent successes in exploiting both latent factor and word
embedding models in recommendation, we propose a novel Regularized
Multi-Embedding (RME) based recommendation model that simultaneously
encapsulates the following ideas via decomposition: (1) which items a user
likes, (2) which two users co-like the same items, (3) which two items users
often co-liked, and (4) which two items users often co-disliked. In
experimental validation, the RME outperforms competing state-of-the-art models
in both explicit and implicit feedback datasets, significantly improving
Recall@5 by 5.9~7.0%, NDCG@20 by 4.3~5.6%, and MAP@10 by 7.9~8.9%. In addition,
under the cold-start scenario for users with the lowest number of interactions,
against the competing models, the RME outperforms NDCG@5 by 20.2% and 29.4% in
MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source
code are available at: https://github.com/thanhdtran/RME.git.Comment: CIKM 201
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model
Clicking data, which exists in abundance and contains objective user
preference information, is widely used to produce personalized recommendations
in web-based applications. Current popular recommendation algorithms, typically
based on matrix factorizations, often have high accuracy and achieve good
clickthrough rates. However, diversity of the recommended items, which can
greatly enhance user experiences, is often overlooked. Moreover, most
algorithms do not produce interpretable uncertainty quantifications of the
recommendations. In this work, we propose the Bayesian Mallows for Clicking
Data (BMCD) method, which augments clicking data into compatible full ranking
vectors by enforcing all the clicked items to be top-ranked. User preferences
are learned using a Mallows ranking model. Bayesian inference leads to
interpretable uncertainties of each individual recommendation, and we also
propose a method to make personalized recommendations based on such
uncertainties. With a simulation study and a real life data example, we
demonstrate that compared to state-of-the-art matrix factorization, BMCD makes
personalized recommendations with similar accuracy, while achieving much higher
level of diversity, and producing interpretable and actionable uncertainty
estimation.Comment: 27 page
Scalable and interpretable product recommendations via overlapping co-clustering
We consider the problem of generating interpretable recommendations by
identifying overlapping co-clusters of clients and products, based only on
positive or implicit feedback. Our approach is applicable on very large
datasets because it exhibits almost linear complexity in the input examples and
the number of co-clusters. We show, both on real industrial data and on
publicly available datasets, that the recommendation accuracy of our algorithm
is competitive to that of state-of-art matrix factorization techniques. In
addition, our technique has the advantage of offering recommendations that are
textually and visually interpretable. Finally, we examine how to implement our
technique efficiently on Graphical Processing Units (GPUs).Comment: In IEEE International Conference on Data Engineering (ICDE) 201
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