Towards Serendipity for Content–Based Recommender Systems

Saat, Nur Izyan Yasmin; Noah, Shahrul Azman Mohd; Mohd, Masnizah

doi:10.18517/ijaseit.8.4-2.6807

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Other potential future works include the addressing the elements of serendipity in recommendation. Exploiting friend's relationship is expected to improve elements of serendipity whereby items suggested are considered novel, relevant and unexpected [36].…”

Section: Discussionmentioning

confidence: 99%

Collaborative Item Recommendations Based on Friendship Strength in Social Network

Jamil¹,

Noah²,

Mohd³

2020

IJMLC

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Recommender Systems (RSs) are among the solutions in addressing the information overload problems. One of the RS main problem is cold start users where RSs do not have enough information to identify the user's preferences and thus unable to recommend relevant items. One of the approaches to overcome such problem is through social relationship which can be extracted from social networks. Existing studies used friendship relation to find nearest neighbors combined with implicit data such as tweet content, posts, like or tags to identify user's preference. This study proposed friendship strength through user's interaction and item rating values to identify users' preferences. User-based collaborative filtering methods are used in developing the system. Two datasets are synthetically obtained from MovieLens database for user's rating information. Meanwhile Twitter users' interactions data, was retrieved from Higgs Boson topic available from the Standford University. There are four phases of development namely user profile phase, friendship strength phase, user's preference phase and items recommendation phase. The findings of the study show that there is an improvement in the proposed method compared to the baseline based on the recall (R), precision (P) and F score measures.

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Section: Discussionmentioning

confidence: 99%

Collaborative Item Recommendations Based on Friendship Strength in Social Network

Jamil¹,

Noah²,

Mohd³

2020

IJMLC

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“…This method uses external sources like Wikipedia to reveal hidden links between items and thus weight the edges linking them, the aim being to increase the likelihood that users will view recommendations made to them as non-obvious. As noted in [12] and [20], this method generates recommendations that are not biased towards popular items, but does not consider user preferences.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Estimating serendipity in content-based recommender systems

Hertz

Kuflik

Tuval

2023

Preprint

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Recommender systems provide personalized recommendations to their users for items and services. They do that using a model that is tailored to each user to infer their preferences based on their characteristics and previous interactions they have made with the system. Recent research suggests that users of a recommender system may like to receive suggestions that provide a pleasant surprise. In other words, a recommendation may be unexpected to the user, but it must be useful. This concept, called serendipity, is one of the aspects that have been proposed to meet user expectations for the recommendations they receive. Introducing serendipity means going beyond the `more of the same' aspect that past recommender systems are criticized for.In this article, we first show how to estimate user preferences based on ratings they have done in the past in a content-based recommender system.This estimation allows us to measure the relevance of a recommendation. We then determine the item attributes that play an important role in the relevance measure.Experiments in the movie domain show that the greater the relevance of a recommendation, the more the users seem willing to discover items having attributes with which they are not familiar, as long as these do not play an important role in their ratings.

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“…Additionally, it is conducive to incorporating new items and exhibits transparency. However, it has certain limitations, such as the cold-start problem for users, restricted content analysis, and potential overspecialisation [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Content-Based Recommendation Using Topic Modelling and Knowledge Graph

Saat,

Noah,

Mohd

2024

ELEKTRON ELEKTROTECH

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Content-based (CB) recommendation algorithms recommend items to a user based on items the user liked in the past. CB methodologies have gained attention due to their higher accuracy and transparency and the emergence of new technologies, such as knowledge graphs (KGs), advances in natural language processing (NLP), and sentiment analysis. While most previous studies have mainly focussed on the use of term frequency-inverse document frequency (TF-IDF) and other related enhancements, little work can be found on using KGs in CB recommendations. This paper presents an enhancement of the conventional CB recommendation by incorporating KGs for a movie domain. The graph is constructed using the MovieLens data set, which is augmented with additional features such as actors, directors, and genres. Furthermore, the graph is expanded by incorporating topics derived from latent dirichlet allocation (LDA) extraction. Using the KGs, the proposed approach enhances user profiles by leveraging the interconnected user-movie relationship within a graph structure. The results of the experiments showed that the proposed approach exceeded the tested baselines in terms of precision, recall, and F-score metrics.

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Towards Serendipity for Content–Based Recommender Systems

Cited by 6 publications

References 22 publications

Collaborative Item Recommendations Based on Friendship Strength in Social Network

Collaborative Item Recommendations Based on Friendship Strength in Social Network

Estimating serendipity in content-based recommender systems

Enhanced Content-Based Recommendation Using Topic Modelling and Knowledge Graph

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