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

New Frontiers In Christian Fiction

Christian fiction must strive for a new quality of writing and be willing to go beyond what is safe if it is going to have an impact outside the church. Although such fiction may include language and situations that are not acceptable among Christians, it will glorify God by presenting the world as he sees it and grappling with deep spiritual issues rather than using cliches to brush over them. Librarians should support quality Christian fiction by purchasing it, recommending it and praying for those in the publishing industry

A Probabilistic Model for the Cold-Start Problem in Rating Prediction using Click Data

One of the most efficient methods in collaborative filtering is matrix factorization, which finds the latent vector representations of users and items based on the ratings of users to items. However, a matrix factorization based algorithm suffers from the cold-start problem: it cannot find latent vectors for items to which previous ratings are not available. This paper utilizes click data, which can be collected in abundance, to address the cold-start problem. We propose a probabilistic item embedding model that learns item representations from click data, and a model named EMB-MF, that connects it with a probabilistic matrix factorization for rating prediction. The experiments on three real-world datasets demonstrate that the proposed model is not only effective in recommending items with no previous ratings, but also outperforms competing methods, especially when the data is very sparse.Comment: ICONIP 201

Relationship based Entity Recommendation System

With the increase in usage of the internet as a place to search for information, the importance of the level of relevance of the results returned by search engines have increased by many folds in recent years. In this paper, we propose techniques to improve the relevance of results shown by a search engine, by using the kinds of relationships between entities a user is interested in. We propose a technique that uses relationships between entities to recommend related entities from a knowledge base which is a collection of entities and the relationships with which they are connected to other entities. These relationships depict more real world relationships between entities, rather than just simple “is-a” or “has-a” relationships. The system keeps track of relationships on which user is clicking and uses this click count as a preference indicator to recommend future entities. This approach is very useful in modern day semantic web searches for recommending entities of user’s interests

The Diffusion of Regulatory Oversight

The idea of cost-benefit analysis has been spreading internationally for centuries — at least since an American named Benjamin Franklin wrote a letter in 1772 to his British friend, Joseph Priestley, recommending that Priestley weigh the pros and cons of a difficult decision in what Franklin dubbed a “moral or prudential algebra” (Franklin 1772) (more on this letter below). Several recent studies show that the use of benefit-cost analysis (BCA), for both public projects and public regulation of private activities, is now unfolding in countries on every habitable continent around the world (Livermore and Revesz 2013; Quah and Toh 2012; De Francesco 2012; Livermore 2011; Cordova-Novion and Jacobzone 2011). This global diffusion of BCA is intermingled with the global diffusion of regulatory capitalism, in which privatized market actors are supervised by expert regulatory agencies (Levi-Faur 2005; Simmons et al. 2008), and with the international spread of ex ante regulatory precautions to anticipate and prevent risks despite uncertainty (Wiener et al. 2011)

Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation

Recommending users with preferred point-of-interests (POIs) has become an important task for location-based social networks, which facilitates users' urban exploration by helping them filter out unattractive locations. Although the influence of geographical neighborhood has been studied in the rating prediction task (i.e. regression), few work have exploited it to develop a ranking-oriented objective function to improve top-N item recommendations. To solve this task, we conduct a manual inspection on real-world datasets, and find that each individual's traits are likely to cluster around multiple centers. Hence, we propose a co-pairwise ranking model based on the assumption that users prefer to assign higher ranks to the POIs near previously rated ones. The proposed method can learn preference ordering from non-observed rating pairs, and thus can alleviate the sparsity problem of matrix factorization. Evaluation on two publicly available datasets shows that our method performs significantly better than state-of-the-art techniques for the top-N item recommendation task