50,259 research outputs found
The Impact of Semantic Context Cues on the User Acceptance of Tag Recommendations: An Online Study
In this paper, we present the results of an online study with the aim to shed
light on the impact that semantic context cues have on the user acceptance of
tag recommendations. Therefore, we conducted a work-integrated social
bookmarking scenario with 17 university employees in order to compare the user
acceptance of a context-aware tag recommendation algorithm called 3Layers with
the user acceptance of a simple popularity-based baseline. In this scenario, we
validated and verified the hypothesis that semantic context cues have a higher
impact on the user acceptance of tag recommendations in a collaborative tagging
setting than in an individual tagging setting. With this paper, we contribute
to the sparse line of research presenting online recommendation studies.Comment: 2 pages, poste
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Item cold-start is a classical issue in recommender systems that affects
anime and manga recommendations as well. This problem can be framed as follows:
how to predict whether a user will like a manga that received few ratings from
the community? Content-based techniques can alleviate this issue but require
extra information, that is usually expensive to gather. In this paper, we use a
deep learning technique, Illustration2Vec, to easily extract tag information
from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE
(Blended Alternate Least Squares with Explanation), a new model for
collaborative filtering, that benefits from this extra information to recommend
mangas. We show, using real data from an online manga recommender system called
Mangaki, that our model improves substantially the quality of recommendations,
especially for less-known manga, and is able to provide an interpretation of
the taste of the users.Comment: 6 pages, 3 figures, 1 table, accepted at the MANPU 2017 workshop,
co-located with ICDAR 2017 in Kyoto on November 10, 201
Collaborative Filtering in Social Tagging Systems Based on Joint Item-Tag Recommendations
Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism - as opposed to Web search - for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based recommendation method. While most of the existing research either implicitly or explicitly assumes a simple tripartite graph structure for this purpose, we propose a comprehensive information structure to capture all types of co-occurrence information in the tagging data. Based on the proposed information structure, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by our proposed user profile, we propose a novel framework for collaborative filtering in social tagging systems. In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the wisdom from the crowd and projected to the item space for final item recommendations. Evaluation using three real-world datasets shows that our proposed recommendation approach significantly outperformed state-of-the-art approaches
Semantic Grounding Strategies for Tagbased Recommender Systems
Recommender systems usually operate on similarities between recommended items
or users. Tag based recommender systems utilize similarities on tags. The tags
are however mostly free user entered phrases. Therefore, similarities computed
without their semantic groundings might lead to less relevant recommendations.
In this paper, we study a semantic grounding used for tag similarity calculus.
We show a comprehensive analysis of semantic grounding given by 20 ontologies
from different domains. The study besides other things reveals that currently
available OWL ontologies are very narrow and the percentage of the similarity
expansions is rather small. WordNet scores slightly better as it is broader but
not much as it does not support several semantic relationships. Furthermore,
the study reveals that even with such number of expansions, the recommendations
change considerably.Comment: 13 pages, 5 figure
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
Recommendation with multi-source heterogeneous information
© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Network embedding has been recently used in social network recommendations by embedding low-dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information. Second, network embedding and item recommendations are learned in two independent steps without any interaction. To this end, we in this paper consider item recommendations based on heterogeneous information sources. Specifically, we combine item structure, textual content and tag information for recommendation. To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source Deep Network Embedding (CDNE for short) is proposed to learn different latent representations. Experimental results on two real-world data sets demonstrate that CDNE can use network representation learning to boost the recommendation performance
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