92,002 research outputs found
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model
Clicking data, which exists in abundance and contains objective user
preference information, is widely used to produce personalized recommendations
in web-based applications. Current popular recommendation algorithms, typically
based on matrix factorizations, often have high accuracy and achieve good
clickthrough rates. However, diversity of the recommended items, which can
greatly enhance user experiences, is often overlooked. Moreover, most
algorithms do not produce interpretable uncertainty quantifications of the
recommendations. In this work, we propose the Bayesian Mallows for Clicking
Data (BMCD) method, which augments clicking data into compatible full ranking
vectors by enforcing all the clicked items to be top-ranked. User preferences
are learned using a Mallows ranking model. Bayesian inference leads to
interpretable uncertainties of each individual recommendation, and we also
propose a method to make personalized recommendations based on such
uncertainties. With a simulation study and a real life data example, we
demonstrate that compared to state-of-the-art matrix factorization, BMCD makes
personalized recommendations with similar accuracy, while achieving much higher
level of diversity, and producing interpretable and actionable uncertainty
estimation.Comment: 27 page
Efficient Diversification of Web Search Results
In this paper we analyze the efficiency of various search results
diversification methods. While efficacy of diversification approaches has been
deeply investigated in the past, response time and scalability issues have been
rarely addressed. A unified framework for studying performance and feasibility
of result diversification solutions is thus proposed. First we define a new
methodology for detecting when, and how, query results need to be diversified.
To this purpose, we rely on the concept of "query refinement" to estimate the
probability of a query to be ambiguous. Then, relying on this novel ambiguity
detection method, we deploy and compare on a standard test set, three different
diversification methods: IASelect, xQuAD, and OptSelect. While the first two
are recent state-of-the-art proposals, the latter is an original algorithm
introduced in this paper. We evaluate both the efficiency and the effectiveness
of our approach against its competitors by using the standard TREC Web
diversification track testbed. Results shown that OptSelect is able to run two
orders of magnitude faster than the two other state-of-the-art approaches and
to obtain comparable figures in diversification effectiveness.Comment: VLDB201
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Top-k Route Search through Submodularity Modeling of Recurrent POI Features
We consider a practical top-k route search problem: given a collection of
points of interest (POIs) with rated features and traveling costs between POIs,
a user wants to find k routes from a source to a destination and limited in a
cost budget, that maximally match her needs on feature preferences. One
challenge is dealing with the personalized diversity requirement where users
have various trade-off between quantity (the number of POIs with a specified
feature) and variety (the coverage of specified features). Another challenge is
the large scale of the POI map and the great many alternative routes to search.
We model the personalized diversity requirement by the whole class of
submodular functions, and present an optimal solution to the top-k route search
problem through indices for retrieving relevant POIs in both feature and route
spaces and various strategies for pruning the search space using user
preferences and constraints. We also present promising heuristic solutions and
evaluate all the solutions on real life data.Comment: 11 pages, 7 figures, 2 table
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