Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline

Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent. At the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content to user interests. By releasing an original dataset of 160 million Tweets along with engagement information, Twitter aims to address exactly that. During this release, special attention is drawn on maintaining compliance with existing privacy laws. Apart from user privacy, this paper touches on the key challenges faced by researchers and professionals striving to predict user engagements. It further describes the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset.Comment: 16 pages, 2 table

Detecting dressing failures using temporal–relational visual grammars

Evaluation of dressing activities is essential in the assessment of the performance of patients with psycho-motor impairments. However, the current practice of monitoring dressing activity (performed by the patients in front of the therapist) has a number of disadvantages when considering the personal nature of dressing activity as well as inconsistencies between the recorded performance of the activity and performance of the same activity carried out in the patients’ natural environment, such as their home. As such, a system that can evaluate dressing activities automatically and objectively would alleviate some of these issues. However, a number of challenges arise, including difficulties in correctly identifying garments, their position in the body (partially of fully worn) and their position in relation to other garments. To address these challenges, we have developed a novel method based on visual grammars to automatically detect dressing failures and explain the type of failure. Our method is based on the analysis of image sequences of dressing activities and only requires availability of a video recording device. The analysis relies on a novel technique which we call temporal–relational visual grammar; it can reliably recognize temporal dressing failures, while also detecting spatial and relational failures. Our method achieves 91% precision in detecting dressing failures performed by 11 subjects. We explain these results and discuss the challenges encountered during this work

Time-aware adaptive tweets ranking through deep learning

Generally, tweets about brands, news and so forth, are mostly delivered to the Twitter user in a reverse chronological order choosing among those twitted by the so-called followed users. Recently, Twitter is facing with information overload by introducing new filtering features, such as “while you are away” in order to show only a few tweets summarizing the posted ones, and ranking the tweets considering the quality, in addition to timeliness. Trivially enough we state that the strategy to rank the tweets to maximize the user engagement and, why not, augmenting the tweet and re-tweet rates, is not unique. There are several dimensions affecting the ranking, such as time, location, semantic, publisher authority, quality, and so on. We point out that the tweet ranking model should vary according to the user's context, interests and how those change along the timeline, cyclically, weekly or at specific date-time when the user logs in. In this work, we introduce a deep learning method attempting to re-adapt the ranking of the tweets by preferring those that are more likely interesting for the user. User's interests are extracted by mainly considering previous user re-tweets, replies and also the time when they occurred. We evaluate a ranking model by measuring how many tweets that will be re-tweeted in the near future were included in the top-ranked tweet list. The results of the proposed ranking model revealed good performances overcoming the methods that consider only the reverse-chronological order or user's interest score. In addition, we pointed out that in our dataset the most impacting features on the performance of proposed ranking model are: publisher authority, tweet content measures, and time-awareness

Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing

This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks