Ronny Lempel

Automatic construction of travel itineraries using social breadcrumbs

Vacation planning is one of the frequent---but nonetheless laborious---tasks that people engage themselves with online; requiring skilled interaction with a multitude of resources. This paper constructs intra-city travel itineraries automatically by tapping a latent source reflecting geo-temporal breadcrumbs left by millions of tourists. For example, the popular rich media sharing site, Flickr, allows photos to be stamped by the time of when they were taken and be mapped to Points Of Interests (POIs) by geographical (i.e. latitude-longitude) and semantic (e.g., tags) metadata.n Leveraging this information, we construct itineraries following a two-step approach. Given a city, we first extract photo streams of individual users. Each photo stream provides estimates on where the user was, how long he stayed at each place, and what was the transit time between places. In the second step, we aggregate all user photo streams into a POI graph. Itineraries are then automatically constructed from the graph based on the popularity of the POIs and subject to the users time and destination constraints.n We evaluate our approach by constructing itineraries for several major cities and comparing them, through a crowd-sourcing marketplace (Amazon Mechanical Turk), against itineraries constructed from popular bus tours that are professionally generated. Our extensive survey-based user studies over about 450 workers on AMT indicate that high quality itineraries can be automatically constructed from Flickr data.

Beyond basic faceted search

This paper extends traditional faceted search to support richer information discovery tasks over more complex data models. Our first extension adds exible, dynamic business intelligence aggregations to the faceted application, enabling users to gain insight into their data that is far richer than just knowing the quantities of documents belonging to each facet. We see this capability as a step toward bringing OLAP capabilities, traditionally supported by databases over relational data, to the domain of free-text queries over metadata-rich content. Our second extension shows how one can efficiently extend a faceted search engine to support correlated facets - a more complex information model in which the values associated with a document across multiple facets are not independent. We show that by reducing the problem to a recently solved tree-indexing scenario, data with correlated facets can be efficiently indexed and retrieved

Adaptive bootstrapping of recommender systems using decision trees

Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Rapid profiling of new users by a recommender system is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. The elicitation process becomes particularly effective when adapted to users responses, making best use of users time by dynamically modifying the questions to improve the evolving profile. In particular, we advocate a specialized version of decision trees as the most appropriate tool for this task. We detail an efficient tree learning algorithm, specifically tailored to the unique properties of the problem. Several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method. We implemented our methods within a movie recommendation service. The experimental study delivered encouraging results, with the tree-based bootstrapping process significantly outperforming previous approaches.

Care to comment?: recommendations for commenting on news stories

Many websites provide commenting facilities for users to express their opinions or sentiments with regards to content items, such as, videos, news stories, blog posts, etc. Previous studies have shown that user comments contain valuable information that can provide insight on Web documents and may be utilized for various tasks. This work presents a model that predicts, for a given user, suitable news stories for commenting. The model achieves encouraging results regarding the ability to connect users with stories they are likely to comment on. This provides grounds for personalized recommendations of stories to users who may want to take part in their discussion. We combine a content-based approach with a collaborative-filtering approach (utilizing users co-commenting patterns) in a latent factor modeling framework. We experiment with several variations of the models loss function in order to adjust it to the problem domain. We evaluate the results on two datasets and show that employing co-commenting patterns improves upon using content features alone, even with as few as two available comments per story. Finally, we try to incorporate available social network data into the model. Interestingly, the social data does not lead to substantial performance gains, suggesting that the value of social data for this task is quite negligible.

On bootstrapping recommender systems

Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may be the quickest to abandon the system when disappointed. Rapid profiling of new users is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. This work offers a new bootstrapping method, which is based on a concrete optimization goal, thereby handily outperforming known approaches in our tests.

Caching search engine results over incremental indices

A Web search engine must update its index periodically to incorporate changes to the Web, and we argue in this work that index updates fundamentally impact the design of search engine result caches. Index updates lead to the problem of cache invalidation: invalidating cached entries of queries whose results have changed. To enable efficient invalidation of cached results, we propose a framework for developing invalidation predictors and some concrete predictors. Evaluation using Wikipedia documents and a query log from Yahoo! shows that selective invalidation of cached search results can lower the number of query re-evaluations by as much as 30% compared to a baseline time-to-live scheme, while returning results of similar freshness.

Constructing travel itineraries from tagged geo-temporal breadcrumbs

Vacation planning is a frequent laborious task which requires skilled interaction with a multitude of resources. This paper develops an end-to-end approach for constructing intra-city travel itineraries automatically by tapping a latent source reflecting geo-temporal breadcrumbs left by millions of tourists. In particular, the popular rich media sharing site, Flickr, allows photos to be stamped by the date and time of when they were taken, and be mapped to Points Of Interest (POIs) by latitude-longitude information as well as semantic metadata (e.g., tags) that describe them.n Our extensive user study on a crowd-sourcing marketplace (Amazon Mechanical Turk), indicates that high quality itineraries can be automatically constructed from Flickr data, when compared against popular professionally generated bus tours.

Expediting search trend detection via prediction of query counts

The massive volume of queries submitted to major Web search engines reflects human interest at a global scale. While the popularity of many search queries is stable over time or fluctuates with periodic regularity, some queries experience a sudden and ephemeral rise in popularity that is unexplained by their past volumes. Typically the popularity surge is precipitated by some real-life event in the news cycle. Such queries form what are known as search trends. All major search engines, using query log analysis and other signals, invest in detecting such trends. The goal is to surface trends accurately, with low latency relative to the actual event that sparked the trend.n This work formally defines precision, recall and latency metrics related to top-k search trend detection. Then, observing that many trend detection algorithms rely on query counts, we develop a linear auto-regression model to predict future query counts. Subsequently, we tap the predicted counts to expedite search trend detection by plugging them into an existing trend detection scheme.n Experimenting with query logs from a major Web search engine, we report both the stand-alone accuracy of our query count predictions, as well as the task-oriented effects of the prediction on the emitted trends. We show an average reduction in trend detection latency of roughly twenty minutes, with a negligible impact on the precision and recall metrics.

Caching for realtime search

Modern search engines feature real-time indices, which incorporate changes to content within seconds. As search engines also cache search results for reducing user latency and back-end load, without careful real-time management of search results caches, the engine might return stale search results to users despite the efforts invested in keeping the underlying index up to date. A recent paper proposed an architectural component called CIP - the cache invalidation predictor. CIPs invalidate supposedly stale cache entries upon index modifications. Initial evaluation showed the ability to keep the performance benefits of caching without sacrificing much the freshness of search results returned to users. However, it was conducted on a synthetic workload in a simplified setting, using many assumptions. We propose new CIP heuristics, and evaluate them in an authentic environment - on the real evolving corpus and query stream of a large commercial news search engine. Our CIPs operate in conjunction with realistic cache settings, and we use standard metrics for evaluating cache performance. We show that a classical cache replacement policy, LRU, completely fails to guarantee freshness over time, whereas our CIPs serve 97% of the queries with fresh results. Our policies incur a negligible impact on the baselines cache hit rate, in contrast with traditional age-based invalidation, which must severely reduce the cache performance in order to achieve the same freshness. We demonstrate that the computational overhead of our algorithms is minor, and that they even allow reducing the caches memory footprint.

Approximately optimal facet selection

Multifaceted search is a popular interaction paradigm that allows users to analyze and navigate through multidimensional data. A crucial aspect of faceted search applications is selecting the list of facets to display to the user following each query. We call this the facet selection problem.n When refining a query by drilling down into a facet, documents that are associated with that facet are promoted in the rankings, as better-ranking documents not associated with the facet are filtered out. We formulate facet selection as an optimization problem aiming to maximize the rank promotion of certain documents. As the optimization problem is NP-Hard, we propose an approximation algorithm for selecting an approximately optimal set of facets per query.n We conducted experiments over hundreds of queries and search results of a large commercial search engine, comparing two flavors of our algorithm to facet selection algorithms appearing in the literature. The results show that our algorithm significantly outperforms those baseline schemes.