Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests

Abstract

Collaborative Filtering is largely applied to personalize item recommendation but its performance is a ected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve recommendation by extracting latent factors from the rating matrices, or by exploiting trust relations established among users in social networks.In this work, we are interested in evaluating whether other sources of preference information than ratings and social ties can be used to improve recommendation performance. Speci cally, we aim at testing whether the integration of frequently co-occurring interests in information search logs can improve recommendation performance in User-to-User Collaborative Filtering (U2UCF). For this purpose, we propose the Extended Category-based Collaborative Filtering (ECCF) recommender, which enriches category-based user pro les derived from the analysis of rating behavior with data categories that are frequently searched together by people in search sessions. We test our model using a big rating dataset and a log of a largely used search engine to extract the co-occurrence of interests. e experiments show that ECCF outperforms U2UCF and categorybased collaborative recommendation in accuracy, MRR, diversity of recommendations and user coverage. Moreover, it outperforms the SVD++ Matrix Factorization algorithm in accuracy and diversity of recommendation lists. CCS CONCEPTS•Information systems → Recommender systems; •Humancentered computing → Collaborative Filtering; KEYWORDSTag-based recommender systems; Collaborative Filtering; Categorybased user pro les; Preference co-occurrence in information search.

Intra-list similarity and human diversity perceptions of recommendations: the details matter

“…As described earlier, ILS is based on pairwise similarity comparisons, and a higher ILS score denotes a lower level of diversity. Other names for ILS are intra-list diversity (Vargas et al 2014;Mauro and Ardissono 2019) or intra-list distance (Lin et al 2020).…”

Improving cold-start recommendations using item-based stereotypes

“…For the rating-prediction task, evaluation is usually based on error metrics such as root mean squared error (RMSE) or mean absolute error (MAE). Rating prediction continues to be an important performance evaluation aspect of RS and has been adopted by recent research (Mauro and Ardissono 2019;Wibowo et al 2018). Nevertheless, researchers have acknowledged that accuracy of rating predictions alone is not sufficient for identifying a quality RS, and the ongoing trend is to evaluate ranked lists of items, presenting users with ranked lists of items and evaluating which RS-derived lists possess qualities such as being relevant and novel to the user.…”

“…Recent work on clustering for RS indicates its popularity as a method for enhancing recommendation quality (Rimaz et al 2019). It is important to note that the majority of the clustering-, similarity-and dimensionality-reduction approaches developed for filtering-based systems or to solve cold-start problems all operate on the user-to-item preferences (or ratings) matrix (Du et al 2017;Felício et al 2016Felício et al , 2017Kluver and Konstan 2014;Mauro and Ardissono 2019;Mirbakhsh and Ling 2018;O'Connor and Herlocker 1999;Sacharidis 2017;Sollenborn and Funk 2002;Shani et al 2007;Wibowo et al 2018). Recently, groupings of users and items have been performed via neural networks-driven text embedding, like word2vec doc2vec, leading to an algorithm capable of grouping users and items via their metadata.…”