A Hybrid Personalized Scientific Paper Recommendation Approach Integrating Public Contextual Metadata

Abstract

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.

Scientific paper recommendation systems: a literature review of recent publications

“…Exceptions to these observations are e.g. found with Bereczki [ 19 ], Nishioka et al [ 74 – 76 ] and Sakib et al [ 88 ].…”

“… When considering the broadest definition of graph-based methods many recent paper recommendation systems tend to belong to the class of hybrid methods. Most of the approaches [ 5 , 46 , 48 , 49 , 57 , 88 , 105 , 117 ] utilise some type of graph structure information as part of the approach which would classify them as graph-based but as they also utilise historic user-interaction data or descriptions of paper features (see, e.g. Li et al [ 57 ] who describe their approach as network-based while using a graph structure, textual components and user profiles) which would render them as either CF or CBF also.…”

“…Instead they measure similarity of candidate papers to an input paper based on co-occurred or co-occurring papers. In a follow-up work with a bigger research group Sakib et al [ 88 ] combine contents of titles, keywords and abstracts with their previously mentioned collaborative filtering approach. They again utilise second-level citation relationships between papers to find correlated publications.…”

“…Table 12 holds information on the different numbers of authors for papers and the percentage of papers out of the 64 observed ones which are authored by groups of this size. We only encountered little cooperation between different co-author groups (see Haruna et al [ 39 ] and Sakib et al [ 88 ] for an exception). There were several groups not extending their previous work [ 121 , 122 ].…”

“…An exception to this general rule are systems which contain a web crawling step for data (see e.g. Ahmad and Afzal [ 2 ] or Sakib et al [ 88 ]). Even with this type of approaches the combination of datasets and their diverse data formats is not identified as a problem.…”