Recommender systems (RSs) have been popular in variety of application domains due to the increased demand for filtering and sorting items and information. Today, there is a numerous approaches and algorithms of data filtering and recommendations. This works presents a conceptual framework for constructing a mobile RS in hyper-local news domain. The mobile RS is designed to deal with specific requirements of news readers, such as spatial- temporal relevance, recency, real-time update and validated news. The implementation of the RS in a distributed file system is also discussed.
In Big Data era, information has increased at an unprecedented rate and the information overload problem has become increasingly severe for online users. Nighty percent of all the data available today were created in the last two years [30]. In this context, RS plays an important role to bring meaningful and relevant information to individuals and business organizations [1]. Starting from mid 1990s, RSs became an independent research area with a large application domain, including e-commerce, multimedia, work and productivity, news, education and tourism [1][2][3]. This work investigates the feasibility of applying a RS to a hyper-local news mobile application.
The mission of a hyper-local news editor is to deliver relevant news to users as quick as possible, considering its location context. Hyper-local news is targeted at or consumed by people or entities that are located within a well-defined area, generally on the scale of a street, a neighborhood, a community or a city. Hyperlocal content must also be relevant in time. The higher the content scores on these dimensions the more relevant the content becomes to the individual and the less it becomes to the masses.
Mobile apps for hyper-local news are becoming popular in software startup scenes around the world, such as BlogFeed 1 , Ripple 2 and MittMedia3 to name a few. However, these startups are also facing with challenges of making sense out of the large volume of data occurring in a real-time manner. We are particularly interested in RSs for mobile application. Besides the mentioned concerns, mobile RSs face a challenge of making accurate recommendations using simple, yet appealing user interface [27]. Most of the mobile RSs heavily rely on locations of the users to recommend items to them, which is also essential in hyper-local news domain. Alternatively, the recommendation is made based on not only item’s content but also user’s context variables, i.e. geographical location and time.
This paper proposes a conceptual architecture of a mobile news RS applied for hyper-local news with user-generated contents and social network data. Our solution adopts different recommendation techniques and considers mobile-specific factors, such as geographical location and temporal information. We also discussed the proposed RS in big data perspective.
The paper is structured as follows: Section 2 presents background about recommendation approaches and challenges in recommending news. Section 3 describes requirements to a hyper-local news RS and Section 4 discuses its conceptual architecture. Finally, Section 5 concludes the paper with future perspectives.
State-of-the-art recommendation approaches can be summarized as in Table 1. Traditional recommendation approaches are classified into content based filtering, collaborative filtering and hybrid approaches [1,2]. Content-based filtering [6][7][8] utilizes several characteristics of a rated item to recommend a future item with similar characteristics. For example, a user selects a movie with a specific genre, IMDb score, and editor’s evaluation. A content-based RS will probably recommend a movie with the same genre, IMDb score and editor’s judgment for the user. Certain characteristics of an item, i.e. textual description, linked images or sound can be analyzed to find the similarity among items.
Collaborative filtering (CF) produces a recommendation using both user’s past preference and also similar decisions made by other users [3][4][5]. The CF technique can be divided into user-based and item-based CF approaches [9]. In the user-based CF approach, a user will receive recommendations of items liked by similar users. In the item-based CF approach, a user will receive recommendations of items that are similar to those they were preferred in the past. Hybrid approaches combine multiple RS techniques to achieve a synergy between them. Several researchers have
Hyper-local News, Conceptual Framework, Norwegian Big Data Symposium (NOBIDS), Trondheim, Norway attempted to combine CF and content-based approaches in order to smoothen their disadvantages and to gain better performance [10][11][12][13].
Recent advancement in RS considers additional characteristics of user reading [14][15][16][17][18]. Social-based RSs utilize social interactions among users, which are available in Internet, to improve the effectiveness of traditional recommendation mechanisms. The social interactions include online friending, making social comments, social tags, etc. Other types of social relations are also used for recommendation generation, i.e. social bookmarks, physical context, social tags, and “co-authorship” relations [14][15][16][17][18]. Particularly, trust based system puts a weight on the opinions of an user which is a friend or a person that the user can trust [14,15].
Context-aware RSs utilize information such as time, geometrical information, or the company of other people (friends, families or colleagues for example),
This content is AI-processed based on open access ArXiv data.
