An adversarial imitation click model for information retrieval

Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback. Click models, which study how users interact with a ranked list of items, provide a useful understanding of user feedback for learning ranking models. Constructing "right"dependencies is the key of any successful click model. However, probabilistic graphical models (PGMs) have to rely on manually assigned dependencies, and oversimplify user behaviors. Existing neural network based methods promote PGMs by enhancing the expressive ability and allowing flexible dependencies, but still suffer from exposure bias and inferior estimation. In this paper, we propose a novel framework, Adversarial Imitation Click Model (AICM), based on imitation learning. Firstly, we explicitly learn the reward function that recovers users' intrinsic utility and underlying intentions. Secondly, we model user interactions with a ranked list as a dynamic system instead of one-step click prediction, alleviating the exposure bias problem. Finally, we minimize the JS divergence through adversarial training and learn a stable distribution of click sequences, which makes AICM generalize well across different distributions of ranked lists. A theoretical analysis has indicated that AICM reduces the exposure bias from O(T2) to O(T). Our studies on a public web search dataset show that AICM not only outperforms state-of-the-art models in traditional click metrics but also achieves superior performance in addressing the exposure bias and recovering the underlying patterns of click sequences

Modeling interactive memex-like applications based on self-modifiable petri nets

This paper introduces an interactive Memex-like application using a self-modifiable Petri Net model – Self-modifiable Color Petri Net (SCPN). The Memex (“memory extender”) device proposed by Vannevar Bush in 1945 focused on the problems of “locating relevant information in the published records and recording how that information is intellectually connected.” The important features of Memex include associative indexing and retrieval. In this paper, the self-modifiable functions of SCPN are used to achieve trail recording and retrieval. A place in SCPN represents a website and an arc indicates the trail direction. Each time when a new website is visited, a place corresponding to this website will be added. After a trail is built, users can use it to retrieve the websites they have visited. Besides, useful user interactions are supported by SCPN to achieve Memex functions. The types of user interactions include: forward, backward, history, search, etc. A simulator has been built to demonstrate that the SCPN model can realize Memex functions. Petri net instances can be designed to model trail record, back, and forward operations using this simulator. Furthermore, a client-server based application system has been built. Using this system, a user can surf online and record his surfing history on the server according to different topics and share them with other users

Verifying Web Applications: From Business Level Specifications to Automated Model-Based Testing

One of reasons preventing a wider uptake of model-based testing in the industry is the difficulty which is encountered by developers when trying to think in terms of properties rather than linear specifications. A disparity has traditionally been perceived between the language spoken by customers who specify the system and the language required to construct models of that system. The dynamic nature of the specifications for commercial systems further aggravates this problem in that models would need to be rechecked after every specification change. In this paper, we propose an approach for converting specifications written in the commonly-used quasi-natural language Gherkin into models for use with a model-based testing tool. We have instantiated this approach using QuickCheck and demonstrate its applicability via a case study on the eHealth system, the national health portal for Maltese residents.Comment: In Proceedings MBT 2014, arXiv:1403.704

Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance

Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network

Personalized recommender systems rely on each user's personal usage data in the system, in order to assist in decision making. However, privacy policies protecting users' rights prevent these highly personal data from being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model on multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the synthetic data in training recommender system models for cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape