14,892 research outputs found
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Spatial item recommendation has become an important means to help people
discover interesting locations, especially when people pay a visit to
unfamiliar regions. Some current researches are focusing on modelling
individual and collective geographical preferences for spatial item
recommendation based on users' check-in records, but they fail to explore the
phenomenon of user interest drift across geographical regions, i.e., users
would show different interests when they travel to different regions. Besides,
they ignore the influence of public comments for subsequent users' check-in
behaviors. Specifically, it is intuitive that users would refuse to check in to
a spatial item whose historical reviews seem negative overall, even though it
might fit their interests. Therefore, it is necessary to recommend the right
item to the right user at the right location. In this paper, we propose a
latent probabilistic generative model called LSARS to mimic the decision-making
process of users' check-in activities both in home-town and out-of-town
scenarios by adapting to user interest drift and crowd sentiments, which can
learn location-aware and sentiment-aware individual interests from the contents
of spatial items and user reviews. Due to the sparsity of user activities in
out-of-town regions, LSARS is further designed to incorporate the public
preferences learned from local users' check-in behaviors. Finally, we deploy
LSARS into two practical application scenes: spatial item recommendation and
target user discovery. Extensive experiments on two large-scale location-based
social networks (LBSNs) datasets show that LSARS achieves better performance
than existing state-of-the-art methods.Comment: Accepted by KDD 201
Exploring Latent Semantic Factors to Find Useful Product Reviews
Online reviews provided by consumers are a valuable asset for e-Commerce
platforms, influencing potential consumers in making purchasing decisions.
However, these reviews are of varying quality, with the useful ones buried deep
within a heap of non-informative reviews. In this work, we attempt to
automatically identify review quality in terms of its helpfulness to the end
consumers. In contrast to previous works in this domain exploiting a variety of
syntactic and community-level features, we delve deep into the semantics of
reviews as to what makes them useful, providing interpretable explanation for
the same. We identify a set of consistency and semantic factors, all from the
text, ratings, and timestamps of user-generated reviews, making our approach
generalizable across all communities and domains. We explore review semantics
in terms of several latent factors like the expertise of its author, his
judgment about the fine-grained facets of the underlying product, and his
writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet
Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii)
item facets, and (iii) review helpfulness. Large-scale experiments on five
real-world datasets from Amazon show significant improvement over
state-of-the-art baselines in predicting and ranking useful reviews
Item Recommendation with Evolving User Preferences and Experience
Current recommender systems exploit user and item similarities by
collaborative filtering. Some advanced methods also consider the temporal
evolution of item ratings as a global background process. However, all prior
methods disregard the individual evolution of a user's experience level and how
this is expressed in the user's writing in a review community. In this paper,
we model the joint evolution of user experience, interest in specific item
facets, writing style, and rating behavior. This way we can generate individual
recommendations that take into account the user's maturity level (e.g.,
recommending art movies rather than blockbusters for a cinematography expert).
As only item ratings and review texts are observables, we capture the user's
experience and interests in a latent model learned from her reviews, vocabulary
and writing style. We develop a generative HMM-LDA model to trace user
evolution, where the Hidden Markov Model (HMM) traces her latent experience
progressing over time -- with solely user reviews and ratings as observables
over time. The facets of a user's interest are drawn from a Latent Dirichlet
Allocation (LDA) model derived from her reviews, as a function of her (again
latent) experience level. In experiments with five real-world datasets, we show
that our model improves the rating prediction over state-of-the-art baselines,
by a substantial margin. We also show, in a use-case study, that our model
performs well in the assessment of user experience levels
Attentive Aspect Modeling for Review-aware Recommendation
In recent years, many studies extract aspects from user reviews and integrate
them with ratings for improving the recommendation performance. The common
aspects mentioned in a user's reviews and a product's reviews indicate indirect
connections between the user and product. However, these aspect-based methods
suffer from two problems. First, the common aspects are usually very sparse,
which is caused by the sparsity of user-product interactions and the diversity
of individual users' vocabularies. Second, a user's interests on aspects could
be different with respect to different products, which are usually assumed to
be static in existing methods. In this paper, we propose an Attentive
Aspect-based Recommendation Model (AARM) to tackle these challenges. For the
first problem, to enrich the aspect connections between user and product,
besides common aspects, AARM also models the interactions between synonymous
and similar aspects. For the second problem, a neural attention network which
simultaneously considers user, product and aspect information is constructed to
capture a user's attention towards aspects when examining different products.
Extensive quantitative and qualitative experiments show that AARM can
effectively alleviate the two aforementioned problems and significantly
outperforms several state-of-the-art recommendation methods on top-N
recommendation task.Comment: Camera-ready manuscript for TOI
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