Structuring Wikipedia Articles with Section Recommendations

Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for humans, especially for newcomers or less experienced editors, as it requires significant knowledge about how a well-written article looks for each possible topic. Inspired by this need, the present paper defines the problem of section recommendation for Wikipedia articles and proposes several approaches for tackling it. Our systems can help editors by recommending what sections to add to already existing or newly created Wikipedia articles. Our basic paradigm is to generate recommendations by sourcing sections from articles that are similar to the input article. We explore several ways of defining similarity for this purpose (based on topic modeling, collaborative filtering, and Wikipedia's category system). We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category-based approach works best, achieving precision@10 of about 80% in the human evaluation.Comment: SIGIR '18 camera-read

Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information such as product reviews and news titles, leading to hybrid filtering methods. Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. In real-world life, no single service can satisfy a user's all information needs. Thus it motivates us to exploit both auxiliary and source information for RSs in this paper. We propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH) methods for cross-domain recommendation with unstructured text in an end-to-end manner. TMH attentively extracts useful content from unstructured text via a memory module and selectively transfers knowledge from a source domain via a transfer network. On two real-world datasets, TMH shows better performance in terms of three ranking metrics by comparing with various baselines. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape

Toward a collective intelligence recommender system for education

The development of Information and Communication Technology (ICT), have revolutionized the world and have moved us into the information age, however the access and handling of this large amount of information is causing valuable time losses. Teachers in Higher Education especially use the Internet as a tool to consult materials and content for the development of the subjects. The internet has very broad services, and sometimes it is difficult for users to find the contents in an easy and fast way. This problem is increasing at the time, causing that students spend a lot of time in search information rather than in synthesis, analysis and construction of new knowledge. In this context, several questions have emerged: Is it possible to design learning activities that allow us to value the information search and to encourage collective participation?. What are the conditions that an ICT tool that supports a process of information search has to have to optimize the student's time and learning? This article presents the use and application of a Recommender System (RS) designed on paradigms of Collective Intelligence (CI). The RS designed encourages the collective learning and the authentic participation of the students. The research combines the literature study with the analysis of the ICT tools that have emerged in the field of the CI and RS. Also, Design-Based Research (DBR) was used to compile and summarize collective intelligence approaches and filtering techniques reported in the literature in Higher Education as well as to incrementally improving the tool. Several are the benefits that have been evidenced as a result of the exploratory study carried out. Among them the following stand out: • It improves student motivation, as it helps you discover new content of interest in an easy way. • It saves time in the search and classification of teaching material of interest. • It fosters specialized reading, inspires competence as a means of learning. • It gives the teacher the ability to generate reports of trends and behaviors of their students, real-time assessment of the quality of learning material. The authors consider that the use of ICT tools that combine the paradigms of the CI and RS presented in this work, are a tool that improves the construction of student knowledge and motivates their collective development in cyberspace, in addition, the model of Filltering Contents used supports the design of models and strategies of collective intelligence in Higher Education.Postprint (author's final draft

An architecture for life-long user modelling

In this paper, we propose a united architecture for the creation of life-long user profiles. Our architecture combines different steps required for a user prole, including feature extraction and representation, reasoning, recommendation and presentation. We discuss various issues that arise in the context of life-long profiling

Intent-Aware Contextual Recommendation System

Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users. Finding out tendencies or intents of a user is a difficult problem to solve. Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web-application as well as determine the intent of the user in each session. We devised a way to encode the user's activity through the sessions. Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques. The aspect of intent awareness (or scoring) is dealt with at this stage. Finally, combining the user activity data with the contextual information gives the recommendation score. The final recommendations are then ranked using filtering and collaborative recommendation techniques to show the top-k recommendations to the user. A provision for feedback is also envisioned in the current system which informs the model to update the various weights in the recommender system. Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations. We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool and the results are better than the baselines. We also tuned certain aspects of our model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining (ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field cannot be longer than 1,920 characters," the abstract appearing here is slightly shorter than the one in the PDF fil

Improving Reachability and Navigability in Recommender Systems

In this paper, we investigate recommender systems from a network perspective and investigate recommendation networks, where nodes are items (e.g., movies) and edges are constructed from top-N recommendations (e.g., related movies). In particular, we focus on evaluating the reachability and navigability of recommendation networks and investigate the following questions: (i) How well do recommendation networks support navigation and exploratory search? (ii) What is the influence of parameters, in particular different recommendation algorithms and the number of recommendations shown, on reachability and navigability? and (iii) How can reachability and navigability be improved in these networks? We tackle these questions by first evaluating the reachability of recommendation networks by investigating their structural properties. Second, we evaluate navigability by simulating three different models of information seeking scenarios. We find that with standard algorithms, recommender systems are not well suited to navigation and exploration and propose methods to modify recommendations to improve this. Our work extends from one-click-based evaluations of recommender systems towards multi-click analysis (i.e., sequences of dependent clicks) and presents a general, comprehensive approach to evaluating navigability of arbitrary recommendation networks

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation