Mobile search has recently been shown to be the major contributor to the growing search market. The key difference between mobile search and desktop search is that information presentation is limited to the screen space of the mobile device. Thus, major search engines have adopted a new type of search result presentation, known as , in which each card presents summarized results from one domain vertical, for a given query, to augment the standard blue-links search results. While it has been widely acknowledged that information cards are particularly suited to mobile user experience, it is also challenging to optimize such result sets. Typically, user engagement metrics like query reformulation are based on whole ranked list of cards for each query and most traditional learning to rank algorithms require per-item relevance labels. In this paper, we investigate the possibility of interpreting query reformulation into effective relevance labels for query-card pairs. We inherit the concept of conventional learning-to-rank, and propose pointwise, pairwise and listwise interpretations for query reformulation. In addition, we propose a learning-to-label strategy that learns the contribution of each card, with respect to a query, where such contributions can be used as labels for training card ranking models. We utilize a state-of-the-art ranking model and demonstrate the effectiveness of proposed mechanisms on a large-scale mobile data from a major search engine, showing that models trained from labels derived from user engagement can significantly outperform ones trained from human judgment labels.
Information Cards : To our knowledge, there is no prior work on the problem of card ranking in mobile search. One of the latest work by Shokouhi and Guo @cite_16 was among the first to present the problem of serving cards to mobile users. However, their work and follow-up papers like @cite_4 addressed the problem under the search setting, i.e., generating card recommendations queries from the user. Our work, on the other hand, address the typical search problem in which the cards are ranked in response to the user's explicit queries.
@cite_16 @cite_4
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The growing accessibility of mobile devices has substantially reformed the way users access information. While the reactive search by query remains as common as before, recent years have witnessed the emergence of various proactive systems such as Google Now and Microsoft Cortana. In these systems, relevant content is presented to users based on their context without a query. Interestingly, despite the increasing popularity of such services, there is very little known about how users interact with them. In this paper, we present the first study on user interactions with information cards. We demonstrate that the usage patterns of these cards vary depending on time and location. We also show that while overall different topics are clicked by users on proactive and reactive platforms, the topics of the clicked documents by the same user tend to be consistent cross-platform. Furthermore, we propose a supervised framework for re-ranking proactive cards based on the user's context and past history. To train our models, we use the viewport duration and clicks to infer pseudo-relevance labels for the cards. Our results suggest that the quality of card ranking can be significantly improved particularly when the user's reactive search history is leveraged and matched against the proactive data about the cards. With the increasing popularity of location-based services, we have accumulated a lot of location data on the Web. In this paper, we are interested in answering two popular location-related queries in our daily life: (1) if we want to do something such as sightseeing or dining in a large city like Beijing, where should we go? (2) If we want to visit a place such as the Bird@?s Nest in Beijing Olympic park, what can we do there? We develop a mobile recommendation system to answer these queries. In our system, we first model the users@? location and activity histories as a user-location-activity rating tensor. Because each user has limited data, the resulting rating tensor is essentially very sparse. This makes our recommendation task difficult. In order to address this data sparsity problem, we propose three algorithms based on collaborative filtering. The first algorithm merges all the users@? data together, and uses a collective matrix factorization model to provide general recommendation (, 2010 [3]). The second algorithm treats each user differently and uses a collective tensor and matrix factorization model to provide personalized recommendation (, 2010 [4]). The third algorithm is a new algorithm which further improves our previous two algorithms by using a ranking-based collective tensor and matrix factorization model. Instead of trying to predict the missing entry values as accurately as possible, it focuses on directly optimizing the ranking loss w.r.t. user preferences on the locations and activities. Therefore, it is more consistent with our ultimate goal of ranking locations activities for recommendations. For these three algorithms, we also exploit some additional information, such as user-user similarities, location features, activity-activity correlations and user-location preferences, to help the CF tasks. We extensively evaluate our algorithms using a real-world GPS dataset collected by 119 users over 2.5 years. We show that all our three algorithms can consistently outperform the competing baselines, and our newly proposed third algorithm can also outperform our other two previous algorithms. With the exponentially growing amount of information available on the Internet, the task of retrieving documents of interest has become increasingly difficult. Search engines usually return more than 1,500 results per query, yet out of the top twenty results, only one half turn out to be relevant to the user. One reason for this is that Web queries are in general very short and give an incomplete specification of individual users' information needs. This paper explores ways of incorporating users' interests into the search process to improve the results. The user profiles are structured as a concept hierarchy of 4,400 nodes. These are populated by 'watching over a user's shoulder' while he is surfing. No explicit feedback is necessary. The profiles are shown to converge and to reflect the actual interests quite well. One possible deployment of the profiles is investigated: re-ranking and filtering search results. Increases in performance are moderate but noticeable and show that fully automatic creation of large hierarchical user profiles is possible. Most Learning To Rank (LTR) algorithms like Ranking SVM, RankNet, LambdaRank and LambdaMART use only relevance label judgments as ground truth for training. But in common scenarios like ranking of information cards (google now, other personal assistants), mobile notifications, netflix recommendations, etc. there is additional information which can be captured from user behavior and how user interacts with the retrieved items. Within the relevance labels, there might be different sets whose information (i.e. cluster information) can be derived implicitly from user interaction (positive, negative, neutral, etc.) or from explicit-user feedback ('Do not show again', 'I like this suggestion', etc). This additional information provides significant knowledge for training any ranking algorithm using two-dimensional output variable. This paper proposes a novel method to use the relevance label along with cluster information to better train the ranking models. Results for user-trial Notification Ranking dataset and standard datasets like LETOR 4.0, MSLR-WEB10K and YahooLTR further support this claim.