103,981 research outputs found
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
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
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the
presented items, plays a key role in online services. With two-decade rapid
development, nowadays the cumulated user behavior sequences on mature Internet
service platforms have become extremely long since the user's first
registration. Each user not only has intrinsic tastes, but also keeps changing
her personal interests during lifetime. Hence, it is challenging to handle such
lifelong sequential modeling for each individual user. Existing methodologies
for sequential modeling are only capable of dealing with relatively recent user
behaviors, which leaves huge space for modeling long-term especially lifelong
sequential patterns to facilitate user modeling. Moreover, one user's behavior
may be accounted for various previous behaviors within her whole online
activity history, i.e., long-term dependency with multi-scale sequential
patterns. In order to tackle these challenges, in this paper, we propose a
Hierarchical Periodic Memory Network for lifelong sequential modeling with
personalized memorization of sequential patterns for each user. The model also
adopts a hierarchical and periodical updating mechanism to capture multi-scale
sequential patterns of user interests while supporting the evolving user
behavior logs. The experimental results over three large-scale real-world
datasets have demonstrated the advantages of our proposed model with
significant improvement in user response prediction performance against the
state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets:
https://github.com/alimamarankgroup/HPM
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