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Algorithms
Special Issues
Collaborative Filtering and Recommender Systems
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Collaborative Filtering and Recommender Systems

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 12872

Special Issue Editor

Dr. Julian J. McAuley

E-Mail Website1 Website2
Guest Editor
Department of Computer Science and Engineering, University of California, San Diego, CA 92093, USA
Interests: social networks; data mining; recommender systems

Special Issue Information

Dear Colleagues,

Every day, we interact with predictive systems that seek to model our behavior, monitor our activities, and make recommendations: Whom will we befriend? What articles will we like? Who influences us in our social network? And do our activities change over time? Models that answer such questions drive important real-world systems, and at the same time are of basic scientific interest to economists, linguists, and social scientists, among others. Recommender Systems and Collaborative Filtering algorithms seek to model such problems, while incorporating ideas from related areas including social network analysis, time-series modeling, and natural language processing.

The open access journal Algorithms will host a Special Issue on “Collaborative Filtering Algorithms”. The goal of the Special Issue is to collect new ideas and techniques related to the design and analysis of recommender systems and collaborative filtering algorithms, covering topics including approximation algorithms, scalability, usability and interpretability, and deployment.

Dr. Julian J. McAuley
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Recommender Systems
  • Collaborative Filtering
  • Personalized Ranking
  • User Behavior
  • Web Mining
  • Temporal, Geospatial and Social Data
  • Natural Language Processing
  • Computational Advertising
  • Computational Social Science

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

17 pages, 529 KiB  
Article
Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
by Qingyao Ai, Vahid Azizi, Xu Chen and Yongfeng Zhang
Algorithms 2018, 11(9), 137; https://doi.org/10.3390/a11090137 - 13 Sep 2018
Cited by 319 | Viewed by 12047
Abstract
Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms—especially the collaborative filtering (CF)- based approaches with shallow or deep models—usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or [...] Read more.
Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms—especially the collaborative filtering (CF)- based approaches with shallow or deep models—usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the availability of vast amounts of data and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users’ historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines. Full article
(This article belongs to the Special Issue Collaborative Filtering and Recommender Systems)
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