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Parts 5 & 6: WWW 2018 tutorial on Understanding User Needs & Tasks

Rishabh Mehrotra
Rishabh Mehrotra

WWW 2018 tutorial on Understanding User Needs & Tasks Details: https://task-ir.github.io/Task-based-Search/

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Inferring User Tasks and Needs Rishabh Mehrotra1, Emine Yilmaz2, Ahmed Hassan Awadallah3 1Spotify, London 2University College London 3Microsoft Research
Outline of the Tutorial Section 1: Introduction Section 2: Characterizing Tasks Section 3: Tasks Extraction Algorithms Section 4: Task based Evaluation Section 5: Applications
Section 5: Applications Task based personalization Task based recommendations Task Tours Predicting Task Continuation Task Completion Dialogue Systems
Task Based Personalization Users tend to be interested in certain tasks when they use search engines Represent users in terms of tasks they are interested in Personalize search results based on that Query recommendation, re-ranking search results,
Traditional Approach: Topic Based Personalization Topics commonly constructed in two ways Manual topical categories (e.g. ODP) Topic Modelling Based (e.g. LDA) Topics
Personalization: Topics versus Tasks Topics Tasks Finance, Basketball, Jazz Finance, Basketball, Pop music Basketball, Pop music
Task based Personalization [Mehrotra and Yilmaz, ACM RecSys Posters 14] Given N users and M tasks Construct an NxM user-task association matrix R Cosine similarity between user profiles and task representations Find the Nxd user feature matrix U, and Mxd task feature matrix T s.t. R U TT Discover the d latent features underlying the interactions between users and tasks
Representing Users in the Task-space Existing user modeling methods fail to differentiate between users having similar topical interests User curious about "search engines" and an experienced IR researcher a stockbroker and a normal investor The objective is to leverage user's topical interest profiles along with user's task associations. Topics Tasks Finance, Basketball, Jazz Finance, Basketball, Pop music Basketball, Pop music
Coupling Topics and Tasks [Mehrotra and Yilmaz, ACM ICTIR15]
Construct a 3-mode tensor to jointly model the user's topical and task preferences: <users, topics, tasks> Define each tensor component as: A user's participation in a certain task gets weighted by her topical affinity. Coupling Topics and Tasks [Mehrotra and Yilmaz, ACM ICTIR15]
Tensor decomposition to leverage connections between different users across different topics and different tasks PARAFAC Tensor Decomposition [Stegeman and Sidiropolous, Linear Algebra and Applications, 07] Ui, Vj, Tk are D-dimensional vectors representing users, topics, and tasks, respectively Discover the D latent features underlying the interactions between users, topics and tasks Coupling Topics and Tasks [Mehrotra and Yilmaz, ACM ICTIR15]
Evaluation: Collaborative Query Recommendation Identify user cohorts based on user preferences Personalize search results based on recommendations from similar users Number of Similar Users
Section 5: Applications Task based personalization Task based recommendations Task Tours Predicting Task Continuation Task Completion Dialogue Systems
Provide heterogeneous recommendations during users browsing process Define tasks as demand sequences embedded in user browsing sessions Task-based Recommendation on a Web-Scale
Step 1: Collaborative Task Mining: extract frequent demand sequences from large scale browser logs achieved via frequent sequence mining problem Step 2: Task-based Demand Prediction predict the upcoming demand of a user given the current browsing session estimate the probability of each demand d D being the follow-on demand of the current session Step 3: Task-based Recommendation Provide site-level recommendations (based on predicted demands) Provide link-level recommendations (heterogeneous recommendations based on browsing behavior) Task-based Recommendation on a Web-Scale
Task Tours: Helping Users Tackle Complex Search Tasks Automatically create multi-step task tours: URL labeling with topical category to identify tasks construction of the task graph that relates tasks to each other building of the tours using task graph identification of triggers Task tours help users: understand the required steps to complete a task, find URLs related to the active task alert users to activities they may have missed Task Tours: Helping Users Tackle Complex Search Tasks, CIKM 2012
Predicting Task Continuation Understand, characterize and detect tasks which will be continued Bing logs used to identify intent, topics & search behavior associated with long running tasks Prediction model using various features Search, Interrupted: Understanding and Predicting Search Task Continuation; SIGIR 2012 Task continuation for broad search intent
Task Completion Dialogue Systems Reinforcement learning based model Goal directed conversations Accesses external knowledge base Slot filling to form a semantic frame End-to-End Task-Completion Neural Dialogue Systems, arXiv 2017
Section 5: Applications Task based personalization Task based recommendations Task Tours Predicting Task Continuation Task Completion Dialogue Systems
Summary - I Query intent understanding Classification based (ODP, LDA) Cluster based (Random walks, reformulations) Session based techniques Time based segmentation Content based segmentation Hybrid segmentation Extracting search tasks Evaluating task extraction algorithms Applications
Query intent understanding Extracting search tasks Task Extraction Clustering based approaches Entity oriented task extraction Structured SVM based bestlinks structures LDA topics with Hawkes process Tasks Subtasks dd-CRP with embeddings model BRT Hierarchical Subtask segmentation Evaluating task extraction algorithms Applications Summary - II
Query intent understanding Extracting search tasks Evaluating task extraction algorithms Gold standard dataset User study based evaluation Alternative techniques TREC Tasks Tracks Applications Summary - III
Query intent understanding Extracting search tasks Evaluating task extraction algorithms Applications Task based user modeling Related Search suggestions Task based ecommerce recommendations Summary - IV
Ongoing/Future Work Task based user satisfaction prediction Digital assistants Task understanding Task completion Book Uber Deliver food Task based recommendations Beyond search web tasks
Questions? Rishabh Mehrotra Research Scientist Spotify, London rishabhm@spotify.com Emine Yilmaz Associate Professor, UCL Faculty Fellow, The Alan Turing Institute Research Consultant, Microsoft Research emine.yilmaz@ucl.ac.uk Ahmed Hassan Awadallah Research Lead Microsoft Research, Redmond hassanam@microsoft.com
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Deadline: 30th November 2017 Notification: 15th December 2017 Workshop: 9th February 2018 aka.ms/wsdm2018-learnir-workshop

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Parts 5 & 6: WWW 2018 tutorial on Understanding User Needs & Tasks