With the recent growth of conversational systems and intelligent assistants such as Apple Siri and Google Assistant, mobile devices are becoming even more pervasive in our lives. As a consequence, users are getting engaged with the mobile apps and frequently search for an information need in their apps. However, users cannot search within their apps through their intelligent assistants. This requires a unified mobile search framework that identifies the target app(s) for the user's query, submits the query to the app(s), and presents the results to the user. In this paper, we take the first step forward towards developing unified mobile search. In more detail, we introduce and study the task of target apps selection, which has various potential real-world applications. To this aim, we analyze attributes of search queries as well as user behaviors, while searching with different mobile apps. The analyses are done based on thousands of queries that we collected through crowdsourcing. We finally study the performance of state-of-the-art retrieval models for this task and propose two simple yet effective neural models that significantly outperform the baselines. Our neural approaches are based on learning high-dimensional representations for mobile apps. Our analyses and experiments suggest specific future directions in this research area.
More recently, research has been done on various topics in mobile IR such as app and venue recommendation as well as app search @cite_8 @cite_5 @cite_7 . For instance, studied query reformulation patterns in mobile query logs and found that users do not tend to switch between voice and text while reformulating their queries. represented apps using online reviews for improved app search on the market. leveraged mobile user gesture interactions, such as touch actions, to predict good search abandonment on mobile search. inferred users implicit intentions from social media for the task of app recommendation. found that fragmented attention of users while searching on-the-go, affects their search objective and performance perception. In contrast to the prior work, we explore how users behave while searching with different apps. To do this, we study the attributes of search queries assigned to different apps.
@cite_5 @cite_7 @cite_8
2532890429 2741898449 2032930526
People often implicitly or explicitly express their needs in social media in the form of "user status text". Such text can be very useful for service providers and product manufacturers to proactively provide relevant services or products that satisfy people's immediate needs. In this paper, we study how to infer a user's intent based on the user's "status text" and retrieve relevant mobile apps that may satisfy the user's needs. We address this problem by framing it as a new entity retrieval task where the query is a user's status text and the entities to be retrieved are mobile apps. We first propose a novel approach that generates a new representation for each query. Our key idea is to leverage social media to build parallel corpora that contain implicit intention text and the corresponding explicit intention text. Specifically, we model various user intentions in social media text using topic models, and we predict user intention in a query that contains implicit intention. Then, we retrieve relevant mobile apps with the predicted user intention. We evaluate the mobile app retrieval task using a new data set we create. Experiment results indicate that the proposed model is effective and outperforms the state-of-the-art retrieval models. Personalized context-aware venue suggestion plays a critical role in satisfying the users' needs on location-based social networks (LBSNs). In this paper, we present a set of novel scores to measure the similarity between a user and a candidate venue in a new city. The scores are based on user's history of preferences in other cities as well as user's context. We address the data sparsity problem in venue recommendation with the aid of a proposed approach to predict contextually appropriate places. Furthermore, we show how to incorporate different scores to improve the performance of recommendation. The experimental results of our participation in the TREC 2016 Contextual Suggestion track show that our approach beats state-of-the-art strategies. Smartphones and tablets with their apps pervaded our everyday life, leading to a new demand for search tools to help users find the right apps to satisfy their immediate needs. While there are a few commercial mobile app search engines available, the new task of mobile app retrieval has not yet been rigorously studied. Indeed, there does not yet exist a test collection for quantitatively evaluating this new retrieval task. In this paper, we first study the effectiveness of the state-of-the-art retrieval models for the app retrieval task using a new app retrieval test data we created. We then propose and study a novel approach that generates a new representation for each app. Our key idea is to leverage user reviews to find out important features of apps and bridge vocabulary gap between app developers and users. Specifically, we jointly model app descriptions and user reviews using topic model in order to generate app representations while excluding noise in reviews. Experiment results indicate that the proposed approach is effective and outperforms the state-of-the-art retrieval models for app retrieval.