Discerning Relevant Model Features in a Content-based Collaborative Recommender System

Abstract

Abstract. Recommender systems suggest users information items they may be interested in. User profiles or usage data are compared with some reference characteristics, which may belong to the items (content-based approach), or to other users in the same context (collaborative filtering approach). These items are usually presented as a ranking, where the more relevant an item is predicted to be for a user, the higher it appears in the ranking. In this scenario, a preferential order has to be inferred, and therefore, preference learning methods can be naturally helpful. The relevant recommendation model features for the learningbased enhancements explored in this work comprise parameters of the recommendation algorithms, and user-related attributes. In the researched approach, machine learning techniques are used to discover which model features are relevant in providing accurate recommendations. The assessment of relevant model features, which is the focus of this paper, is envisioned as the first step in a learning cycle in which improved recommendation models are produced and executed after the discovery step, based on the findings that result from it.

The use of machine learning algorithms in recommender systems: A systematic review

“…ML algorithms, by definition, improve their performance with access to more data. Similarly, the more data that is Baldominos et al, 2015;Banerjee et al, 2012;Bar et al, 2013;Bauer & Nanopoulos, 2014;Bellogín et al, 2011;Biancalana et al, 2011;Braida et al, 2015;Brouard & Pomot, 2016;Buabin, 2012;Cai et al, 2010Cai et al, , 2012Caraballo et al, 2014;Castro-Herrera et al, 2009;Costa et al, 2012Costa et al, , 2013Das et al, 2013;Das Dôres et al, 2016;De Gemmis et al, 2008;Diaby et al, 2013Diaby et al, , 2014Dinuzzo et al, 2011; T.-K. Fan & Chang, 2010;Forsati et al, 2009;Forsati & Meybodi, 2010;Gedikli et al, 2011;Geng et al, 2016;Haiduc et al, 2013;Hernández del Olmo et al, 2009;Hofmann, 2003Hofmann, , 2004Huang & Nikulin, 2014;Hussain et al, 2015;R. Zhang & Tran, 2010;Islam et al, 2015;Jin et al, 2005;Jun, 2005;Jung & Lee, 2004;Kao & Fahn, 2013;Karahodza & Donko, 2015;Kong et al, 2013;…”

“…Song et al, 2011;Verma et al, 2016;Wan et al, 2009;Yan et al, 2013;Yuan et al, 2014;K. Zhao & Pan, 2015) Academic 12 (Das et al, 2013;Hernández del Olmo et al, 2009;Huang & Nikulin, 2014;Krohn-Grimberghe et al, 2011;Luong, Huynh, Gauch, Do, & Hoang, 2012;Middleton et al, 2004;Montañés et al, 2009;Oyama et al, 2012;Pecli et al, 2015;Vialardi et al, 2011;Xin et al, 2014) News 11 (Bellogín et al, 2011;Brouard & Pomot, 2016;Buabin, 2012;Das et al, 2013;Z. Fan et al, 2016; W.-P. Lee & Lu, 2003;Leopairote et al, 2013;Q.-C. Li et al, 2008;Nguyen et al, 2016;Nie et al, 2013;Nicol et al, 2014) E-commerce 10 ( Anaissi & Goyal, 2015;Bauer & Nanopoulos, 2014;Buettner, 2016;Castro-Herrera et al, 2009;R.…”

Ontology-Based Process for Recommending Health WebSites

“…In [10], Oufaida and Nouali present a multi view recommender system that includes collaborative, social and semantic views of the user's profile, related to a set of resources semantically annotated. Recently, in [12], it is presented the construction of a recommender system which is described as an iterative process; where at each iteration a model representing the preferential characteristics for the recommendation is obtained. The system is an ontology-based recommendation process that produces recommendations by applying content-based, context-aware and collaborative criteria.…”

“…Considering the classification of recommendation techniques given by [11], our proposal also matches user profiles and resources, although we rather combine content-based (web content properties) and demographic (user profiles) approaches. Furthermore, some aspects faced by [12], as considering context issues (i.e. the query situation at the moment the user makes a query) and the exploitation of ontological structures that underlie the recommendation process, are also considered in our proposal.…”

Modelling a web site quality‐based recommendation system

“…Recently, in Bellogín et al (2011), it is presented the construction of a recommender system which is described as an iterative process, in each iteration, a model representing the preferential characteristics for the recommendation is obtained. The system is an ontology‐based recommendation process that produces recommendations by applying content‐based, context‐aware and collaborative criteria.…”