Content Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report
Nowadays, most recommender systems exploit user-provided ratings to infer their preferences. However, the growing popularity of social and e-commerce websites has encouraged users to also share comments and opinions through textual reviews. In this paper, we introduce a new recommendation approach which exploits the semantic annotation of user reviews to extract useful and non-trivial information about the items to recommend. It also relies on the knowledge freely available in the Web of Data, notably in DBpedia and Wikidata, to discover other resources connected with the annotated entities. We evaluated our approach in three domains, using both DBpedia and Wikidata. The results showed that our solution provides a better ranking than another recommendation method based on the Web of Data, while it improves in novelty with respect to traditional techniques based on ratings. Additionally, our method achieved a better performance with Wikidata than DBpedia.
Code (0)
등록된 구현이 없습니다.
Tasks
Recommendation SystemsSimilar Papers 제목 키워드 기반
Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric
In this paper, we present our work to support publishers and editors in finding descriptive tags for e-books through tag recommendations. We propose a hybrid tag recommendation system for e-books, which leverages search …
DescriptiveDiversityRecommendation SystemsSemantic Similarity+2On Content-Based Recommendation and User Privacy in Social-Tagging Systems
Recommendation systems and content filtering approaches based on annotations and ratings, essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised conte…
Recommendation SystemsTAGWhat You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations
With over 500 million tweets posted per day, in Twitter, it is difficult for Twitter users to discover interesting content from the deluge of uninteresting posts. In this work, we present a novel, explainable, topical re…
Collaborative FilteringRecommendation SystemsCross-domain User Preference Learning for Cold-start Recommendation
Cross-domain cold-start recommendation is an increasingly emerging issue for recommender systems. Existing works mainly focus on solving either cross-domain user recommendation or cold-start content recommendation. Howev…
Recommendation SystemsVLM4Rec: Multimodal Semantic Representation for Recommendation with Large Vision-Language Models
Multimodal recommendation is commonly framed as a feature fusion problem, where textual and visual signals are combined to better model user preference. However, the effectiveness of multimodal recommendation may depend …
Multimodal Recommendation