9,171 research outputs found
Selection of Clusters based on Internal Indices in Multi-Clustering Collaborative Filtering Recommender System
The successful application of a multi-clustering based neighborhood approach to recommender systems has led to increased recommendation accuracy and the elimination of divergence related to differences in clustering methods traditionally used. The Multi-Clustering Collaborative Filtering algorithm was developed to achieve this, as described in the author’s previous papers. However, utilizing multiple clusters poses challenges regarding memory consumption and scalability.Not all partitionings are equally advantageous, making selecting clusters for the recommender system’s input crucial without compromising recommendation accuracy. This article presents a solution for selecting clustering schemesbased on internal indices evaluation. This method can be employed for preparing input data in collaborative filtering recommender systems. The study’s results confirm the positive impact of scheme selection on the overall recommendationperformance, as it typically improves after the selection process.Furthermore, a smaller number of clustering schemes used asinput for the recommender system enhances scalability andreduces memory consumption. The findings are compared withbaseline recommenders’ outcomes to validate the effectiveness ofthe proposed approach
Neural Attentive Session-based Recommendation
Given e-commerce scenarios that user profiles are invisible, session-based
recommendation is proposed to generate recommendation results from short
sessions. Previous work only considers the user's sequential behavior in the
current session, whereas the user's main purpose in the current session is not
emphasized. In this paper, we propose a novel neural networks framework, i.e.,
Neural Attentive Recommendation Machine (NARM), to tackle this problem.
Specifically, we explore a hybrid encoder with an attention mechanism to model
the user's sequential behavior and capture the user's main purpose in the
current session, which are combined as a unified session representation later.
We then compute the recommendation scores for each candidate item with a
bi-linear matching scheme based on this unified session representation. We
train NARM by jointly learning the item and session representations as well as
their matchings. We carried out extensive experiments on two benchmark
datasets. Our experimental results show that NARM outperforms state-of-the-art
baselines on both datasets. Furthermore, we also find that NARM achieves a
significant improvement on long sessions, which demonstrates its advantages in
modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and
Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939,
arXiv:1606.08117 by other author
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
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