6,230 research outputs found
Session-based Recommendation with Graph Neural Networks
The problem of session-based recommendation aims to predict user actions
based on anonymous sessions. Previous methods model a session as a sequence and
estimate user representations besides item representations to make
recommendations. Though achieved promising results, they are insufficient to
obtain accurate user vectors in sessions and neglect complex transitions of
items. To obtain accurate item embedding and take complex transitions of items
into account, we propose a novel method, i.e. Session-based Recommendation with
Graph Neural Networks, SR-GNN for brevity. In the proposed method, session
sequences are modeled as graph-structured data. Based on the session graph, GNN
can capture complex transitions of items, which are difficult to be revealed by
previous conventional sequential methods. Each session is then represented as
the composition of the global preference and the current interest of that
session using an attention network. Extensive experiments conducted on two real
datasets show that SR-GNN evidently outperforms the state-of-the-art
session-based recommendation methods consistently.Comment: 9 pages, 4 figures, accepted by AAAI Conference on Artificial
Intelligence (AAAI-19
Preference fusion and Condorcet's Paradox under uncertainty
Facing an unknown situation, a person may not be able to firmly elicit
his/her preferences over different alternatives, so he/she tends to express
uncertain preferences. Given a community of different persons expressing their
preferences over certain alternatives under uncertainty, to get a collective
representative opinion of the whole community, a preference fusion process is
required. The aim of this work is to propose a preference fusion method that
copes with uncertainty and escape from the Condorcet paradox. To model
preferences under uncertainty, we propose to develop a model of preferences
based on belief function theory that accurately describes and captures the
uncertainty associated with individual or collective preferences. This work
improves and extends the previous results. This work improves and extends the
contribution presented in a previous work. The benefits of our contribution are
twofold. On the one hand, we propose a qualitative and expressive preference
modeling strategy based on belief-function theory which scales better with the
number of sources. On the other hand, we propose an incremental distance-based
algorithm (using Jousselme distance) for the construction of the collective
preference order to avoid the Condorcet Paradox.Comment: International Conference on Information Fusion, Jul 2017, Xi'an,
Chin
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
Session-based recommendation (SBR) systems aim to utilize the user's
short-term behavior sequence to predict the next item without the detailed user
profile. Most recent works try to model the user preference by treating the
sessions as between-item transition graphs and utilize various graph neural
networks (GNNs) to encode the representations of pair-wise relations among
items and their neighbors. Some of the existing GNN-based models mainly focus
on aggregating information from the view of spatial graph structure, which
ignores the temporal relations within neighbors of an item during message
passing and the information loss results in a sub-optimal problem. Other works
embrace this challenge by incorporating additional temporal information but
lack sufficient interaction between the spatial and temporal patterns. To
address this issue, inspired by the uniformity and alignment properties of
contrastive learning techniques, we propose a novel framework called
Session-based Recommendation with Spatio-Temporal Contrastive Learning Enhanced
GNNs (RESTC). The idea is to supplement the GNN-based main supervised
recommendation task with the temporal representation via an auxiliary
cross-view contrastive learning mechanism. Furthermore, a novel global
collaborative filtering graph (CFG) embedding is leveraged to enhance the
spatial view in the main task. Extensive experiments demonstrate the
significant performance of RESTC compared with the state-of-the-art baselines
e.g., with an improvement as much as 27.08% gain on HR@20 and 20.10% gain on
[email protected]: Under reviewing draft of ACM TOI
Time-aware topic recommendation based on micro-blogs
Topic recommendation can help users deal with the information overload issue in micro-blogging communities. This paper proposes to use the implicit information network formed by the multiple relationships among users, topics and micro-blogs, and the temporal information of micro-blogs to find semantically and temporally relevant topics of each topic, and to profile users' time-drifting topic interests. The Content based, Nearest Neighborhood based and Matrix Factorization models are used to make personalized recommendations. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on a real world dataset that collected from Twitter.com
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