Folksonomy-based user interest and disinterest profiling for improved recommendations: An ontological approach

Abstract

Social tagging has revolutionized the social and personal experience of users across numerous web platforms by enabling the organizing, managing, sharing and searching of web data. The extensive amount of information generated by tagging systems can be utilized for recommendation purposes. However, the unregulated creation of social tags by users can produce a great deal of noise and the tags can be unreliable; thus, exploiting them for recommendation is a nontrivial task. In this study, a new recommender system is proposed based on the similarities between user and item profiles. The approach applied is to generate user and item profiles by discovering tag patterns that are frequently generated by users. These tag patterns are categorized into irrelevant patterns and relevant patterns which represent diverse user preferences in terms of likes and dislikes. Furthermore, presented here is a method for translating these tag-based profiles into semantic profiles by determining the underlying meaning(s) of the tags, and mapping them to semantic entities belonging to external knowledge bases. To alleviate the cold start and overspecialization problems, semantic profiles are enriched in two phases: (a) using a semantic spreading mechanism and then (b) inheriting the preferences of similar users. Experiment indicates that this approach not only provides a better representation of user interests, but also achieves a better recommendation result when compared with existing methods. The performance of the proposed recommendation method is investigated in the face of the cold start problem, the results of which confirm that it can indeed remedy the problem for early adopters, hence improving overall recommendation quality.

Social Metrics Applied to Smart Tourism

“…Other works, like Cantador et al andMoahedian et al (Cantador et al, 2008, Movahedian andKhayyambashi, 2014), are similar to our proposed user model, since we use tags and keywords to build a lax ontology.…”

Semantic tag recommendation based on associated words exploiting the interwiki links of Wikipedia

“…However, Wikipedia, a wide-coverage collaboratively edited encyclopaedia, is utilised in information retrieval, disambiguation, recommender systems, ranking and knowledge acquisition and validation as a knowledge source [13]. Several attempts have been made to filter tags according to the existence of Wikipedia articles [14–16]. They perform filtering by identifying whether each tag has an exact match in entries belonging to Wikipedia.…”

“…In order to overcome the aforementioned problems, various approaches utilising occurrence frequency, probability, statistics or knowledge bases have been proposed for tag recommendations [2,6–20]. The common goal of current works is to increase the understanding of images and reinforce the efficiency and reliability of tag-based image retrieval.…”

Towards a knowledge-based probabilistic and context-aware social recommender system

“…In this context, Semantic Web–based recommendation techniques (techniques that take advantage of ontology or rule-based reasoning capabilities) are often used together with traditional recommendation techniques such as content-based and collaborative filtering (CF) techniques to leverage each other’s strengths while overcoming each other’s weaknesses. This has contributed to the growing popularity of the knowledge-based hybrid recommender systems [14–17].…”