THESIS
2019
ix, 36 pages : illustrations ; 30 cm
Abstract
Session-based recommendation is a task to predict users’ next actions given a sequence
of previous actions in the same session. Existing methods either encode the previous actions
in a strict order or completely ignore the order. It is not necessary to always capture
the sequential information in sessions by following a strict order, because sometimes, the
order of actions in a short sub-sequence, called the detailed order, may not be important,
e.g., when a user is just comparing the same kind of products from different brands. We
term the property that the order of actions in the sub-session level does not matter the
local invariance. Nevertheless, the high-level ordering information is still useful because
the data is sequential in nature. Therefore, a good session-based reco...[
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Session-based recommendation is a task to predict users’ next actions given a sequence
of previous actions in the same session. Existing methods either encode the previous actions
in a strict order or completely ignore the order. It is not necessary to always capture
the sequential information in sessions by following a strict order, because sometimes, the
order of actions in a short sub-sequence, called the detailed order, may not be important,
e.g., when a user is just comparing the same kind of products from different brands. We
term the property that the order of actions in the sub-session level does not matter the
local invariance. Nevertheless, the high-level ordering information is still useful because
the data is sequential in nature. Therefore, a good session-based recommender should
consider the local invariance property while capturing the sequential information by paying
different attention to the ordering information in different levels of granularity. To
this end, we propose a novel model called LINet to automatically ignore the insignificant
detailed ordering information in some sub-sessions, while keeping the high-level sequential
information of the whole sessions. In the model, we first use a full self-attention layer
with Gaussian weighting to extract features of sub-sessions, and then we apply a recurrent
neural network to capture the high-level sequential information. Extensive experiments
on two real-world datasets show that our method outperforms or matches the state-of-the-art methods and the proposed mechanism to consider the local invariance property plays an important role.
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