Final report, independent Study during Fall 2009 "Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles"

This report describes our study of different ways to improve existing collaborative filtering techniques in order to recommend scientific articles. Using data crawled from CiteUlike, a collaborative tagging service for academic purposes, we compared the classical user-based collaborative filtering algorithm as described by Schafer et al. [2], with two enhanced variations: 1) using a tag-based similarity calculation, to avoid depending on ratings to find the neighborhood of a user, and 2) incorporate the amount of raters in the final recommendation ranking to decrease the noise of items that have been rated by too few users. We provide a discussion of our results, describing the dataset and highlighting our findings about applying collaborative filtering on folksonomies instead of the classic bipartite user-item network, and providing guidelines of our future research

USER CONTROLLABILITY IN A HYBRID RECOMMENDER SYSTEM

Since the introduction of Tapestry in 1990, research on recommender systems has traditionally focused on the development of algorithms whose goal is to increase the accuracy of predicting users’ taste based on historical data. In the last decade, this research has diversified, with human factors being one area that has received increased attention. Users’ characteristics, such as trusting propensity and interest in a domain, or systems’ characteristics, such as explainability and transparency, have been shown to have an effect on improving the user experience with a recommender. This dissertation investigates on the role of controllability and user characteristics upon the engagement and experience of users of a hybrid recommender system. A hybrid recommender is a system that integrates the results of different algorithms to produce a single set of recommendations. This research examines whether allowing the user to control the process of fusing or integrating different algorithms (i.e., different sources of relevance) results in increased engagement and a better user experience. The essential contribution of this dissertation is an extensive study of controllability in a hybrid fusion scenario. In particular, the introduction of an interactive Venn diagram visualization, combined with sliders explored in a previous work, can provide an efficient visual paradigm for information filtering with a hybrid recommender that fuses different prospects of relevance with overlapping recommended items. This dissertation also provides a three-fold evaluation of the user experience: objective metrics, subjective user perception, and behavioral measures

Twitter in Academic Conferences: Usage, Networking and Participation over Time

Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze the scholars' Twitter use in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are increasing differences with respect to conversation use and information use in Twitter. We studied the interaction network between users to understand whether assumptions about the structure of the conversations hold over time and between different types of interactions, such as retweets, replies, and mentions. While `people come and people go', we want to understand what keeps people stay with the conference on Twitter. By casting the problem to a classification task, we find different factors that contribute to the continuing participation of users to the online Twitter conference activity. These results have implications for research communities to implement strategies for continuous and active participation among members

Recommending Items in Social Tagging Systems Using Tag and Time Information

In this work we present a novel item recommendation approach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this candidate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation coming from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function. As the results of our extensive evaluation conducted on data-sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.Comment: 6 pages, 2 tables, 9 figure

Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation

Advances in image processing and computer vision in the latest years have brought about the use of visual features in artwork recommendation. Recent works have shown that visual features obtained from pre-trained deep neural networks (DNNs) perform very well for recommending digital art. Other recent works have shown that explicit visual features (EVF) based on attractiveness can perform well in preference prediction tasks, but no previous work has compared DNN features versus specific attractiveness-based visual features (e.g. brightness, texture) in terms of recommendation performance. In this work, we study and compare the performance of DNN and EVF features for the purpose of physical artwork recommendation using transactional data from UGallery, an online store of physical paintings. In addition, we perform an exploratory analysis to understand if DNN embedded features have some relation with certain EVF. Our results show that DNN features outperform EVF, that certain EVF features are more suited for physical artwork recommendation and, finally, we show evidence that certain neurons in the DNN might be partially encoding visual features such as brightness, providing an opportunity for explaining recommendations based on visual neural models.Comment: DLRS 2017 workshop, co-located at RecSys 201

Language, Twitter and Academic Conferences

Using Twitter during academic conferences is a way of engaging and connecting an audience inherently multicultural by the nature of scientific collaboration. English is expected to be the lingua franca bridging the communication and integration between native speakers of different mother tongues. However, little research has been done to support this assumption. In this paper we analyzed how integrated language communities are by analyzing the scholars' tweets used in 26 Computer Science conferences over a time span of five years. We found that although English is the most popular language used to tweet during conferences, a significant proportion of people also tweet in other languages. In addition, people who tweet solely in English interact mostly within the same group (English monolinguals), while people who speak other languages tend to show a more diverse interaction with other lingua groups. Finally, we also found that the people who interact with other Twitter users show a more diverse language distribution, while people who do not interact mostly post tweets in a single language. These results suggest a relation between the number of languages a user speaks, which can affect the interaction dynamics of online communities.Comment: 4 pages, 3 figures, 4 tables, submitted to ACM Hypertext and Social Media 201

New Operator Based on a Multi Support Point Algorithm Applied to Feature Extraction

International audienceIn the context of quality control, we propose a new operator using multi support points to highlight local perturbations on flat surfaces. We compare the design of this operator with different gradients and residues in their ability to extract small perturbations as well as their efficiency on large surface. The operator is defined as a weighted differential operator evaluated on each point. It is designed to extract a region with a high slope followed by a plateau with a given width. It has a low computational complexity and it could be vectorized