883 research outputs found
Leveraging aggregate ratings for improving predictive performance of recommender systems
This paper describes an approach for incorporating externally specified aggregate ratings information
into certain types of recommender systems, including two types of collaborating filtering
and a hierarchical linear regression model. First, we present a framework for incorporating aggregate
rating information and apply this framework to the aforementioned individual rating models.
Then we formally show that this additional aggregate rating information provides more accurate
recommendations of individual items to individual users. Further, we experimentally confirm this
theoretical finding by demonstrating on several datasets that the aggregate rating information
indeed leads to better predictions of unknown ratings. We also propose scalable methods for
incorporating this aggregate information and test our approaches on large datasets. Finally, we
demonstrate that the aggregate rating information can also be used as a solution to the cold start
problem of recommender systems.NYU, Stern School of Business, Center for Digital Economy Researc
Explainable Reasoning over Knowledge Graphs for Recommendation
Incorporating knowledge graph into recommender systems has attracted
increasing attention in recent years. By exploring the interlinks within a
knowledge graph, the connectivity between users and items can be discovered as
paths, which provide rich and complementary information to user-item
interactions. Such connectivity not only reveals the semantics of entities and
relations, but also helps to comprehend a user's interest. However, existing
efforts have not fully explored this connectivity to infer user preferences,
especially in terms of modeling the sequential dependencies within and holistic
semantics of a path. In this paper, we contribute a new model named
Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for
recommendation. KPRN can generate path representations by composing the
semantics of both entities and relations. By leveraging the sequential
dependencies within a path, we allow effective reasoning on paths to infer the
underlying rationale of a user-item interaction. Furthermore, we design a new
weighted pooling operation to discriminate the strengths of different paths in
connecting a user with an item, endowing our model with a certain level of
explainability. We conduct extensive experiments on two datasets about movie
and music, demonstrating significant improvements over state-of-the-art
solutions Collaborative Knowledge Base Embedding and Neural Factorization
Machine.Comment: 8 pages, 5 figures, AAAI-201
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