Sirup

Maccatrozzo, Valentina; Terstall, Manon; Aroyo, Lora; Schreiber, Guus

doi:10.1145/3025171.3025185

Cited by 31 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, according to [1,2,16], serendipity is related to unexpectedness and relevance, while in [6], novelty is included in the definition of serendipity. In [7] a recommendation is considered serendipitous if it is unexpected and interesting for the user, while in [15], it must also be pleasant.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Estimating serendipity in content-based recommender systems

Hertz

Kuflik

Tuval

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Brief Literature Reviewmentioning

confidence: 99%

Estimating serendipity in content-based recommender systems

Hertz

Kuflik

Tuval

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The need in the TV context to avoid sending viewers into a "filter bubble" where they only see more TV that lies within their modelled interests has led to work on diversity and serendipity in recommendations [198,199].…”

Section: Tv Content Personalisationmentioning

confidence: 99%

Data-driven personalisation of television content: a survey

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Yamazaki & Nakajima (2017) stated that the necessarily on develop a recommendation system that involving novelty and serendipity for the system to be better in order to build an advantageous recommender system. This element can trigger positive user interest that motivates the user to follow recommendation (Maccatrozzo et al, 2017). Several systems are using or evaluating serendipity as the engine.…”

Section: Research Backgroundmentioning

confidence: 99%

A Hybrid Serendipity Social Recommender Model

Zolkafly

Ahmad

2020

MJoC

View full text Add to dashboard Cite

Social support is essential, especially in a working environment, because it can reduce psychological strain. The psychological strain associated with mental health in daily lives could have been gained from mismatched staffing and indirect control of the staffs. In order to meditate the problem, in this study, a hybrid serendipity social recommender model is proposed. This model is a combination of several proposed models encountered during the development process. Firstly, Rough Set Theory (RST) has been used in the early development stage to compute an automated attribute selection. RST is a mathematical tool that is widely used for knowledge discovery and feature selection. At the same time, it, minimizes redundancies among variables in classifying objects and extracts rules from the database. In the next stage, the classification model is used to classify the data into subclasses by using a deep learning algorithm. This algorithm aims to define the higher matching suggested attributes and used for processing massive data. Lastly, a reasoning approach is applied by using case-based reasoning from the result produced. The reasoning approach is used to finding the reasons why the attributes are selected. This approach searches the history of the selected attributes or compute a reason by digging back in the database.

show abstract

Sirup

Cited by 31 publications

References 25 publications

Estimating serendipity in content-based recommender systems

Estimating serendipity in content-based recommender systems

Data-driven personalisation of television content: a survey

A Hybrid Serendipity Social Recommender Model

Contact Info

Product

Resources

About