Query Chains: Learning to Rank from Implicit Feedback

This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.Comment: 10 page

On Interpretation and Measurement of Soft Attributes for Recommendation

We address how to robustly interpret natural language refinements (or critiques) in recommender systems. In particular, in human-human recommendation settings people frequently use soft attributes to express preferences about items, including concepts like the originality of a movie plot, the noisiness of a venue, or the complexity of a recipe. While binary tagging is extensively studied in the context of recommender systems, soft attributes often involve subjective and contextual aspects, which cannot be captured reliably in this way, nor be represented as objective binary truth in a knowledge base. This also adds important considerations when measuring soft attribute ranking. We propose a more natural representation as personalized relative statements, rather than as absolute item properties. We present novel data collection techniques and evaluation approaches, and a new public dataset. We also propose a set of scoring approaches, from unsupervised to weakly supervised to fully supervised, as a step towards interpreting and acting upon soft attribute based critiques.publishedVersio

Resolving Indirect Referring Expressions for Entity Selection

Recent advances in language modeling have enabled new conversational systems. In particular, it is often desirable for people to make choices among specified options when using such systems. We address this problem of reference resolution, when people use natural expressions to choose between the entities. For example, given the choice `Should we make a Simnel cake or a Pandan cake?' a natural response from a dialog participant may be indirect: `let's make the green one'. Such natural expressions have been little studied for reference resolution. We argue that robustly understanding such language has large potential for improving naturalness in dialog, recommendation, and search systems. We create AltEntities (Alternative Entities), a new public dataset of 42K entity pairs and expressions (referring to one entity in the pair), and develop models for the disambiguation problem. Consisting of indirect referring expressions across three domains, our corpus enables for the first time the study of how language models can be adapted to this task. We find they achieve 82%-87% accuracy in realistic settings, which while reasonable also invites further advances

Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation

Recommendation systems are ubiquitous yet often difficult for users to control and adjust when recommendation quality is poor. This has motivated the development of conversational recommendation systems (CRSs), with control over recommendations provided through natural language feedback. However, building conversational recommendation systems requires conversational training data involving user utterances paired with items that cover a diverse range of preferences. Such data has proved challenging to collect scalably using conventional methods like crowdsourcing. We address it in the context of item-set recommendation, noting the increasing attention to this task motivated by use cases like music, news and recipe recommendation. We present a new technique, TalkTheWalk, that synthesizes realistic high-quality conversational data by leveraging domain expertise encoded in widely available curated item collections, showing how these can be transformed into corresponding item set curation conversations. Specifically, TalkTheWalk generates a sequence of hypothetical yet plausible item sets returned by a system, then uses a language model to produce corresponding user utterances. Applying TalkTheWalk to music recommendation, we generate over one million diverse playlist curation conversations. A human evaluation shows that the conversations contain consistent utterances with relevant item sets, nearly matching the quality of small human-collected conversational data for this task. At the same time, when the synthetic corpus is used to train a CRS, it improves Hits@100 by 10.5 points on a benchmark dataset over standard baselines and is preferred over the top-performing baseline in an online evaluation

Building A Personalized Tourist Attraction Recommender System Using Crowdsourcing (Demonstration)

ABSTRACT We demonstrate how crowdsourcing can be used to automatically build a personalized tourist attraction recommender system, which tailors recommendations to specific individuals, so different people who use the system each get their own list of recommendations, appropriate to their own traits. Recommender systems crucially depend on the availability of reliable and large scale data that allows predicting how a new individual is likely to rate items from the catalog of possible items to recommend. We show how to automate the process of generating this data using crowdsourcing, so that such a system can be built even when such a dataset is not initially available. We first find possible tourist attractions to recommend by scraping such information from Wikipedia. Next, we use crowdsourced workers to filter the data, then provide their opinions regarding these items. Finally, we use machine learning methods to predict how new individuals are likely to rate each attraction, and recommend the items with the highest predicted ratings

ABSTRACT Improving Personalized Web Search using Result Diversification

We present and evaluate methods for diversifying search results to improve personalized web search. A common personalization approach involves reranking the top N search results such that documents likely to be preferred by the user are presented higher. The usefulness of reranking is limited in part by the number and diversity of results considered. We propose three methods to increase the diversity of the top results and evaluate the effectiveness of these methods

Learning to Rank from Implicit Feedback

Whenever access to information is mediated by a computer, we can easily record how users respond to the information with which they are presented. These normal interactions between users and information systems are implicit feedback. The key question we address is -- how can we use implicit feedback to automatically improve interactive information systems, such as desktop search and Web search? Contrasting with data collected from external experts, which is assumed as input in most previous research on optimizing interactive information systems, implicit feedback gives more accurate and up-to-date data about the needs of actual users. While another alternative is to ask users for feedback directly, implicit feedback collects data from all users, and does not require them to change how they interact with information systems. What makes learning from implicit feedback challenging, is that the behavior of people using interactive information systems is strongly biased in several ways. These biases can obscure the useful information present, and make standard machine learning approaches less effective. This thesis shows that implicit feedback provides a tremendous amount of practical information for learning to rank, making four key contributions. First, we demonstrate that query reformulations can be interpreted to provide relevance information about documents that are presented to users. Second, we describe an experiment design that provably avoids presentation bias, which is otherwise present when recording implicit feedback. Third, we present a Bayesian method for collecting more useful implicit feedback for learning to rank, by actively selecting rankings to show in anticipation of user responses. Fourth, we show how to learn rankings that resolve query ambiguity using multi-armed bandits. Taken together, these contributions reinforce the value of implicit feedback, and present new ways it can be exploited