Determining User Journey Risk Trajectories in Information Seeking Sessions

This disclosure describes techniques to measure risk trajectories in user journeys involving online search tasks performed by a user using a search engine, chatbot, or another query answering engine. Search tasks include the user interacting with (e.g., selecting and viewing) search results, chatbot generated answers, and web pages linked to those search results. Based on metadata about user-submitted queries, the user search session is divided into user visit segments that include sensitive queries by the user relating to seeking assistance (“help seeking”) or seeking potentially detrimental content (“harm seeking”). Determination of risk categories for sensitive queries are made (e.g., by a human evaluator and/or automated system) and a risk trajectory for the user is determined over a user session based on determined risk valuations. The user session is categorized based on risk trajectory to determine potential of risk for harm seeking by the user. Described techniques can measure risk trajectories that include multiple interactions of a user journey and enable improvement in providing assistance to help-seeking and harm-seeking users. The discussion in this paper is the result of exploratory studies conducted to assess risks associated with user journeys - using mental health as a particular example

Measuring User Journey Friction in Search Engines

This disclosure describes techniques to measure user friction in search tasks performed by a user using a search engine. User friction can be defined as the inverse of total time spent in refinement before a user obtains a desired search result. User friction is indicated by the amount of user actions taken before the user obtains a desired search result. Based on user-permitted metadata indicating queries and actions input by the user, a user search session is divided into user visit segments classified based on the query intent of the user, each segment indicating a search task. User friction scores are determined for completed search tasks based on counts of user friction interactions, such as returning to a search results page after selecting a search result or refining a query. The described techniques can measure friction in user search tasks involving multiple interactions in a user journey and can enable improvement in the experience of using a search engine

Modeling multi-query retrieval tasks using density matrix transformation.

The quantum probabilistic framework has recently been applied to Information Retrieval (IR). A representative is the Quantum Language Model (QLM), which is developed for the ad-hoc retrieval with single queries and has achieved significant improvements over traditional language models. In QLM, a density matrix, defined on the quantum probabilistic space, is estimated as a representation of user's search intention with respect to a specific query. However, QLM is unable to capture the dynamics of user's information need in query history. This limitation restricts its further application on the dynamic search tasks, e.g., session search. In this paper, we propose a Session-based Quantum Language Model (SQLM) that deals with multi-query session search task. In SQLM, a transformation model of density matrices is proposed to model the evolution of user's information need in response to the user's interaction with search engine, by incorporating features extracted from both positive feedback (clicked documents) and negative feedback (skipped documents). Extensive experiments conducted on TREC 2013 and 2014 session track data demonstrate the effectiveness of SQLM in comparison with the classic QLM

RGU-ISTI-Essex at TREC 2011 Session Track

Mining query recommendation from query logs has attracted a lot of attention in recent years. We propose to use query recommendations extracted from the logs of a web search engine to solve the session track tasks. The runs are obtained by using the Search Shortcuts recommender system. The Search Shortcuts technique uses an inverted index and the concept of “successful sessions” present in a web search engine’s query log to produce effective recommendations for both frequent and rare/unseen queries. We adapt the above technique as a query expan- sion tool and use it to expand the given queries for Session Track at TREC 2011. The expansion is generated by using a method which aims to consider all past queries in the session. The expansion terms obtained are then used to build a global, uniformly weighted, representation of the user session (RL2). Furthermore, the expansion terms are then combined with a ranked list of results in order to boost terms appearing more frequently in the final results lists (RL3). Finally, we also integrate dwell times and the weighting method obtained taking both result lists and clicks into account for assigning weights to the terms to expand the final query of the session. In addition to that, we submitted a baseline run. It is based on the observation that using the term “wikipedia” to expand the query resulted in a better retrieval performance for the tasks at last year’s session track at TREC 2010

Intent Models for Contextualising and Diversifying Query Suggestions

The query suggestion or auto-completion mechanisms help users to type less while interacting with a search engine. A basic approach that ranks suggestions according to their frequency in the query logs is suboptimal. Firstly, many candidate queries with the same prefix can be removed as redundant. Secondly, the suggestions can also be personalised based on the user's context. These two directions to improve the aforementioned mechanisms' quality can be in opposition: while the latter aims to promote suggestions that address search intents that a user is likely to have, the former aims to diversify the suggestions to cover as many intents as possible. We introduce a contextualisation framework that utilises a short-term context using the user's behaviour within the current search session, such as the previous query, the documents examined, and the candidate query suggestions that the user has discarded. This short-term context is used to contextualise and diversify the ranking of query suggestions, by modelling the user's information need as a mixture of intent-specific user models. The evaluation is performed offline on a set of approximately 1.0M test user sessions. Our results suggest that the proposed approach significantly improves query suggestions compared to the baseline approach.Comment: A short version of this paper was presented at CIKM 201

A Zero Attention Model for Personalized Product Search

Product search is one of the most popular methods for people to discover and purchase products on e-commerce websites. Because personal preferences often have an important influence on the purchase decision of each customer, it is intuitive that personalization should be beneficial for product search engines. While synthetic experiments from previous studies show that purchase histories are useful for identifying the individual intent of each product search session, the effect of personalization on product search in practice, however, remains mostly unknown. In this paper, we formulate the problem of personalized product search and conduct large-scale experiments with search logs sampled from a commercial e-commerce search engine. Results from our preliminary analysis show that the potential of personalization depends on query characteristics, interactions between queries, and user purchase histories. Based on these observations, we propose a Zero Attention Model for product search that automatically determines when and how to personalize a user-query pair via a novel attention mechanism. Empirical results on commercial product search logs show that the proposed model not only significantly outperforms state-of-the-art personalized product retrieval models, but also provides important information on the potential of personalization in each product search session