Titlebook: Bias and Social Aspects in Search and Recommendation; First International Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference proce

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What Kind of Content Are You Prone to Tweet? Multi-topic Preference Model for Tweeters,t of interest in a certain topic is a challenging task, especially considering the massive digital information they are exposed to. For example, in the context of Twitter, aligned with his/her preferences a user may tweet and retweet more about technology than sports and do not share any music-relat

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毗鄰

,The Impact of Foursquare Checkins on Users’ Emotions on Twitter, been studied on users’ behavior. There has been recent work that have focused on how online social network behavior and activity can impact users’ offline behavior. In this paper, we study the inverse where we focus on whether users’ offline behavior captured through their check-ins at different ve

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Improving News Personalization Through Search Logs,est profiles that are matched with news articles in order to properly decide which articles are to be recommended. When constructing user profiles, existing personalization methods exploit the user activity observed within the news service itself without incorporating information from other sources.

helper-T-cells

Analyzing the Interaction of Users with News Articles to Create Personalization Services,by taking advantage of individual preferences, content of news portals can be tailored on the bases of sociological aspects (e.g., the demographics of the users or the region in which they live) elicited from user interactions with the news. This allows to generate personalization with a coarse gran

moribund

Using String-Comparison Measures to Improve and Evaluate Collaborative Filtering Recommender System in this type of system is the Collaborative Filtering which recommends products to users based on their interactions and on what items similar users have liked in the past. However, many traditional methods for determining similarity do not consider temporal information neither the rich information

laxative

Enriching Product Catalogs with User Opinions,nowledge for both manufacturers and customers. However, reviews often bring so much information that exceeds the human capacity of reasoning and hampers their effective use. Thus, researchers on how to organize a large number of opinions available on the reviews in the Web play a substantial role. T

Pituitary-Gland

1865-0929 held in April, 2020. Due to the COVID-19 pandemic BIAS 2020 was held virtually.?.The 10 full papers and 7 short papers were carefully reviewed and seleced from 44 submissions.?The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impac

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