3,313 research outputs found
Dual Contrastive Network for Sequential Recommendation with User and Item-Centric Perspectives
With the outbreak of today's streaming data, sequential recommendation is a
promising solution to achieve time-aware personalized modeling. It aims to
infer the next interacted item of given user based on history item sequence.
Some recent works tend to improve the sequential recommendation via randomly
masking on the history item so as to generate self-supervised signals. But such
approach will indeed result in sparser item sequence and unreliable signals.
Besides, the existing sequential recommendation is only user-centric, i.e.,
based on the historical items by chronological order to predict the probability
of candidate items, which ignores whether the items from a provider can be
successfully recommended. The such user-centric recommendation will make it
impossible for the provider to expose their new items and result in popular
bias.
In this paper, we propose a novel Dual Contrastive Network (DCN) to generate
ground-truth self-supervised signals for sequential recommendation by auxiliary
user-sequence from item-centric perspective. Specifically, we propose dual
representation contrastive learning to refine the representation learning by
minimizing the euclidean distance between the representations of given
user/item and history items/users of them. Before the second contrastive
learning module, we perform next user prediction to to capture the trends of
items preferred by certain types of users and provide personalized exploration
opportunities for item providers. Finally, we further propose dual interest
contrastive learning to self-supervise the dynamic interest from next item/user
prediction and static interest of matching probability. Experiments on four
benchmark datasets verify the effectiveness of our proposed method. Further
ablation study also illustrates the boosting effect of the proposed components
upon different sequential models.Comment: 23 page
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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