scholarly journals Influence of tweets and diversification on serendipitous research paper recommender systems

2020 ◽  
Vol 6 ◽  
pp. e273
Author(s):  
Chifumi Nishioka ◽  
Jörn Hauke ◽  
Ansgar Scherp
Keyword(s):  
Computer Science ◽  
Recommender Systems ◽  
User Satisfaction ◽  
Important Determinant ◽  
Large Body ◽  
Research Paper ◽  
Research Questions ◽  
Evaluation Metric ◽  
Recommendation Strategy ◽  
Research Paper Recommender Systems

In recent years, a large body of literature has accumulated around the topic of research paper recommender systems. However, since most studies have focused on the variable of accuracy, they have overlooked the serendipity of recommendations, which is an important determinant of user satisfaction. Serendipity is concerned with the relevance and unexpectedness of recommendations, and so serendipitous items are considered those which positively surprise users. The purpose of this article was to examine two key research questions: firstly, whether a user’s Tweets can assist in generating more serendipitous recommendations; and secondly, whether the diversification of a list of recommended items further improves serendipity. To investigate these issues, an online experiment was conducted in the domain of computer science with 22 subjects. As an evaluation metric, we use the serendipity score (SRDP), in which the unexpectedness of recommendations is inferred by using a primitive recommendation strategy. The results indicate that a user’s Tweets do not improve serendipity, but they can reflect recent research interests and are typically heterogeneous. Contrastingly, diversification was found to lead to a greater number of serendipitous research paper recommendations.

A Survey on Data Mining Techniques in Research Paper Recommender Systems

2019 ◽  
pp. 119-143 ◽  
Author(s):  
Benard Magara Maake ◽  
Sunday O. Ojo ◽  
Tranos Zuva
Keyword(s):  
Data Mining ◽  
Recommender Systems ◽  
Research Paper ◽  
Distance Measures ◽  
Sampling Techniques ◽  
Hidden Information ◽  
Data Mining Techniques ◽  
Reduction Methods ◽  
Mining Methods ◽  
Research Paper Recommender Systems

In this chapter, the authors give an overview of the main data mining techniques that are utilized in the context of research paper recommender systems. These techniques refer to mathematical models and tools that are utilized in discovering patterns in data. Data mining is a term used to describe a collection of techniques that infer recommendation rules and build models from research paper datasets. The authors briefly describe how research paper recommender systems' data is processed, analyzed, and then, finally, interpreted using these techniques. They review different distance measures, sampling techniques, and dimensionality reduction methods employed in computing research paper recommendations. They also review the various clustering, classification, and association rule-mining methods employed to mine for hidden information. Finally, they highlight the major data mining issues that are affecting research paper recommender systems.

Information Processing in Research Paper Recommender System Classes

2019 ◽  
pp. 90-118
Author(s):  
Benard M. Maake ◽  
Sunday O. Ojo ◽  
Tranos Zuva
Keyword(s):  
Information Processing ◽  
Recommender Systems ◽  
Operational Research ◽  
Data Access ◽  
Research Paper ◽  
Research Community ◽  
The Internet ◽  
Research Papers ◽  
Art Research ◽  
Research Paper Recommender Systems

Research-related publications and articles have flooded the internet, and researchers are in the quest of getting better tools and technologies to improve the recommendation of relevant research papers. Ever since the introduction of research paper recommender systems, more than 400 research paper recommendation related articles have been so far published. These articles describe the numerous tools, methodologies, and technologies used in recommending research papers, further highlighting issues that need the attention of the research community. Few operational research paper recommender systems have been developed though. The main objective of this review paper is to summaries the state-of-the-art research paper recommender systems classification categories. Findings and concepts on data access and manipulations in the field of research paper recommendation will be highlighted, summarized, and disseminated. This chapter will be centered on reviewing articles in the field of research paper recommender systems published from the early 1990s until 2017.

scholarly journals Research-paper recommender systems: a literature survey

2015 ◽  
Vol 17 (4) ◽  
pp. 305-338 ◽  
Author(s):  
Joeran Beel ◽  
Bela Gipp ◽  
Stefan Langer ◽  
Corinna Breitinger
Keyword(s):  
Recommender Systems ◽  
Research Paper ◽  
Literature Survey ◽  
Research Paper Recommender Systems

scholarly journals A Subspace Clustering Framework for Research Group Collaboration

2011 ◽  
pp. 96-116
Author(s):  
Nitin Agarwal ◽  
Ehtesham Haque ◽  
Huan Liu ◽  
Lance Parsons
Keyword(s):  
Recommender Systems ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Subspace Clustering ◽  
Research Paper ◽  
High Dimensional ◽  
Research Papers ◽  
The Core ◽  
Benchmark Datasets ◽  
Research Paper Recommender Systems

Researchers spend considerable time searching for relevant papers on the topic in which they are currently interested. Often, despite having similar interests, researchers in the same lab do not find it convenient to share results of bibliographic searches and thus conduct independent time-consuming searches. Research paper recommender systems can help the researcher avoid such time-consuming searches by allowing each researcher to automatically take advantage of previous searches performed by others in the lab. Existing recommender systems were developed for commercial domains to assist users by focusing towards products of their interests. Unlike those domains, the research paper domain has relatively few users when compared with the huge number of research papers. In this paper we present a novel system to recommend relevant research papers to a user based on the user’s recent querying and browsing habits. The core of the system is a scalable subspace clustering algorithm, SCuBA (Subspace ClUstering Based Analysis) that performs well on the sparse, high-dimensional data collected in this domain. Both synthetic and benchmark datasets are used to evaluate the recommendation system and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers.

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