Where to Go on Your Next Trip? Optimizing Travel Destinations Based on User Preferences

Recommendation based on user preferences is a common task for e-commerce websites. New recommendation algorithms are often evaluated by offline comparison to baseline algorithms such as recommending random or the most popular items. Here, we investigate how these algorithms themselves perform and compare to the operational production system in large scale online experiments in a real-world application. Specifically, we focus on recommending travel destinations at Booking.com, a major online travel site, to users searching for their preferred vacation activities. To build ranking models we use multi-criteria rating data provided by previous users after their stay at a destination. We implement three methods and compare them to the current baseline in Booking.com: random, most popular, and Naive Bayes. Our general conclusion is that, in an online A/B test with live users, our Naive-Bayes based ranker increased user engagement significantly over the current online system.Comment: 6 pages, 2 figures in SIGIR 2015, SIRIP Symposium on IR in Practic

Challenges in recommending venues within smart cities

Recommending venues to a user within a city is a task that has emerged recently with the growing interest in location-based information access. However, the current applications for this task only use the limited and private data gathered by Location-based Social Networks (LBSNs) such as Foursquare or Google Places. In this position paper, we discuss the research opportunities that can arise with the use of the digital infrastructure of a smart city, and how the venue recommendation applications can benefit from this infrastructure. We focus on the potential applications of social and physical sensors for improving the quality of the recommendations, and highlight the challenges in evaluating such recommendations

Current Research in Supporting Complex Search Tasks

ABSTRACT ere is broad consensus in the eld of IR that search is complex in many use cases and applications, both on the Web and in domain speci c collections, and both professionally and in our daily life. Yet our understanding of complex search tasks, in comparison to simple look up tasks, is fragmented at best. e workshop addresses many open research questions: What are the obvious use cases and applications of complex search? What are essential features of work tasks and search tasks to take into account? And how do these evolve over time? With a multitude of information, varying from introductory to specialized, and from authoritative to speculative or opinionated, when to show what sources of information? How does the information seeking process evolve and what are relevant di erences between di erent stages? With complex task and search process management, blending searching, browsing, and recommendations, and supporting exploratory search to sensemaking and analytics, UI and UX design pose an overconstrained challenge. How do we evaluate and compare approaches? Which measures should be taken into account? Supporting complex search tasks requires new collaborations across the elds of CHI and IR, and the proposed workshop will bring together a diverse group of researchers to work together on one of the greatest challenges of our eld

Experiments with a Venue-Centric Model for Personalisedand Time-Aware Venue Suggestion

No abstract available

Design and Evaluation of Temporal Summarization Systems

Temporal Summarization (TS) is a new track introduced as part of the Text REtrieval Conference (TREC) in 2013. This track aims to develop systems which can return important updates related to an event over time. In TREC 2013, the TS track specifically used disaster related events such as earthquake, hurricane, bombing, etc. This thesis mainly focuses on building an effective TS system by using a combination of Information Retrieval techniques. The developed TS system returns updates related to disaster related events in a timely manner. By participating in TREC 2013 and with experiments conducted after TREC, we examine the effectiveness of techniques such as distributional similarity for term expansion, which can be employed in building TS systems. Also, this thesis describes the effectiveness of other techniques such as stemming, adaptive sentence selection over time and de-duplication in our system, by comparing it with other baseline systems. The second part of the thesis examines the current methodology used for evaluating TS systems. We propose a modified evaluation method which could reduce the manual effort of assessors, and also correlates well with the official track’s evaluation. We also propose a supervised learning based evaluation method, which correlates well with the official track’s evaluation of systems and could save the assessor’s time by as much as 80%