Research on Personalized Recommendation System Based on LBS

Research in Electronic Commerce Frontiers (RECF) Volume 2, 2014 www.seipub.org/recf

Research on Personalized Recommendation System Based on LBS Zhao Shou‐xiang1, Wang Chen2 School of Computer and Information Engineering, Beijing Technology and Business University No.11 Fucheng Road, Haidian Distric, Beijing 102488, China

zhao_sx4384_cn@sina.com; 2fendouyc@126.com

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Received 4 December 2013; Accepted 14 January 2014; Published February 2014 © 2014 Science and Engineering Publishing Company

Abstract With the continuous development of mobile communication, more and more users make communications through mobile terminal. The traditional recommendation system doesnʹt take the usersʹ location into account, which leads to a decline in accuracy and personalization. The introduction of the Location Based Service (LBS) can realize the personalized recommendations better. The paper elaborated the structure of the personalized recommendation system based on LBS, and the key points and countermeasures of the systemʹs realization.

Keywords LBS; Recommendation System; Personalized Recommendation

Introduction Since the rapid increase of information, when people use the e‐commerce sites to select services and goods, they are overwhelmed by the vast amounts of information.

This has resulted in a huge search costs and the poor accuracy. Then personalized recommendation system came into being. The system can automatically recommend to users services and goods which they may be interested in, based on the users’ interests and behaviors.

In recent years, mobile networks and mobile communication terminals have continued to evolve, which allows users to access the network anytime, anywhere for seeking services. Traditional personalized recommendation system has been increasingly unable to meet the mobile users’ demand of personalized recommendations. For the user of mobile terminal, the traditional personalized recommendation system will establish a model of ʺUser‐Projectʺ (U‐I), but does not take the impact of the user’s location into account.

Facing with this problem, the technology of LBS is a good attempt to make the recommendation more targeted and personalized.

LBS’s Definition and Characteristics Definition Location Based Service, abbreviated as LBS, determines the location of the user by means of one of several technologies, and then the location and other information used to provide personalized applications and services.

Simply, LBS contains two meanings, that is, location and service; first of which is to determine the location of a mobile terminal or user; followed by providing various information services associated with the location.

Characteristics LBS has the following three prominent features: 1) Location Relevance With the continuous upgrading of mobile terminals, the number of mobile terminal users is growing and people are increasingly dependent on mobile devices to access services. According to the 32nd China Internet Development Statistics Report, in June 2013, the number of mobile phone users is 464 million. As the largest mobile Internet terminal, mobile terminals’ position becomes more stable.

The traditional personalized recommendation system, generally applied on personal computers, is no geographical differences. Recommended result is generated just based on use’s interests and behaviours. Then, the userʹs location is not important for the recommendation process, or even not affected. Unlike PC’s multi‐users, each mobile terminal almost corresponds to a fixed user. Thus, in the LBS environment, the applications can work better for location, identification, information

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collection and processing. And this makes location relevance become a very prominent feature of LBS‐based applications. By pinpoint user’s location, recommendation system can have a more potent personalization.

2) Time Emergency LBS’s occurrence is because of the demand from US, which asks mobile operators to provide emergency rescue services for mobile phone users. So time emergency is the inherent characteristic of LBS. The mobile devices are portable in daily activities. In the event of an emergency, the end user can instantly access appropriate services, such as weather, location, information of the surrounding hotel and neighboring restaurant.

3) Ubiquitous Access One advantage of using mobile devices to access the networks is not subject to restrictions of time and place. As long as it is able to successfully connect to the Internet, the userʹs information will be able to reach a higher synchronization with the network. Because of LBS’s ubiquitous access, the services used LBS are very suitable for time‐critical applications.

Framework of Personalized Recommendation System Based on LBS It can be learnt that from the characteristics of LBS, LBS technology if successfully used in recommender systems, can well improve the accuracy of recommended results. Therefore, generally a framework of personal‐ ized recommendation system is proposed based on LBS, as shown in Figure 1.

FIG. 1 FRAMEWORK OF PERSONALIZED RECOMMENDATION SYSTEM BASED ON LBS

Firstly, the user using the mobile terminal, can locate himself/herself by LBS technology. Secondly, the userʹs profile information can be sent to the recommended server through the network. Then recommended

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server generates result by the combined action of location information server, commodity items server and customers information server. Finally, the result will be pushed back to user through the network. The above process is the panoramic recommended procedure.

Critical Issues of LBS in Recommendation System The unique characteristics of LBS determine that the recommended systems based on LBS, to some extent, are different from traditional recommendation systems.

If we want to use LBS in recommended system successfully, the following three issues must be noted.

Reflect the Changes of Demand Quickly, Raise the Concern of Short‐term Interest Recommendation system based on LBS deals with the problem that the userʹs interests change quickly, due to the constant changes of location and environment change. For example, a same user’s concern will change from the leisure services in afternoon to dining information at nightfall. The traditional recommenda‐ tion systems are usually only concerned with the usersʹ long‐term interest, rather than the changes of short‐term interests. Thus, the recommended system based on LBS should reflect the changes quickly.

Improve the Relevance of Location to Demand, Have a Higher Sensitivity to Situation Compared with the traditional recommendation system, to achieve a high degree of personalization when producing the recommended result, not only the user’s interest and demand, but the recommended algorithm based on LBS also need to take the user’s environmental conditions into account, which is fundamentally different from the traditional. Therefore, the system based on LBS must be as rich and accurate as possible to obtain the userʹs location information, for a highly sensitive to the user’s situation. For example, the recommended results should be different even if the user’s long‐term interest does not change, because of different environments in which user is located in the business district and tourist attraction.

Certainly, the user is more willing to get some shopping information in the business district, and thus information of scenic spot, accommodation and catering in tourist attraction.

Solve the Cold Start Problem Similar to the traditional recommendation system,

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ʺcold startʺ is always considered to be a key issue when designing the recommended algorithm. When a new user or a new item need to be recommended, due to the lack of previous records, it is difficult for the proposed system to produce satisfactory results.

The Feasible Solutions for Critical Issues Build an Interest Model Which Focus on Change of Interests The user’s interest model is built by acquisition and processing the records of behaviors. If the data does not get lost, the user’s information will not be reduced, but only get more and more abundance with the accumulation of time. Among all the recorded data, if most of the data correspond to a same point of interest, then this interest is the user’s long‐term interest. Yet, the personalized recommendation system based on LBS always deals with the demands which change suddenly. So we should pay more attention to the userʹs recent behavior data. Namely, relative to the long‐term, the system need an interest model that can reflect the user’s short‐term interest.

In order to focus the transfer of user interest, the following methods have been proposed: 1) Time Window In this method, the construction of the interest model uses the behavior data within a specified time slot current. In this way, the userʹs short‐term behaviors will receive more attention. And the recommended system will focus on the userʹs recent interest. However, in this method, the size of window needs to set ready in advance. If the size is set inappropriately, it will influence the results.

Even if the window’s size can be adjusted dynamically, but this method still has a fatal drawback; that is, it ignores the behavior information out of the time window. In other words, it ignores the userʹs longer‐term stabilized points of interest. This approach is very defective.

2) Forgetting Model For the disadvantage of ʺTime Windowʺ, we can use the method of weighting. It is believed that peopleʹs interests will decay over time. And those decay slowly adds up to be long‐term interests.

When we give a higher weight value to the recent behavioral data which are near the time from now on the timeline, on the contrary, a lower weight to the relatively distant data, this approach will

neither ignore the long‐term interests, nor solve the problem of insufficient attention to short‐term interests. Such models are called forgotten models.

However, this method also has some disadvantages, namely, forgetting models focus on the short‐term interests by removing the impact of older behaviors, instead of finding new interests actively.

3) Long‐short Term Model For lack of forgetting model, scholars have also proposed long‐short term model. The userʹs interest model is divided into two parts, long‐term and short‐term interest model; both of which will be maintained synchronously, but their focuses are different. The long‐term interest model is responsible for establishing the userʹs general interests which change slowly; while the short‐term interest model is responsible for the establishment of the userʹs special interests which change rapidly.

Taking different approaches for these two models, the system can respond to the userʹs demands better. However, this system must bear the calculation amount of maintaining two models.

Build a Context Model to Improve the Algorithmʹs Locational Sensitivity Traditional recommendation algorithms are concerned only with the userʹs interests and records for recommend results. Such algorithms will form a recommended mode of ʺUsers‐Itemsʺ. The recommendation system based on LBS should increase the importance of context to the same degree of userʹs interests, to form a model of ʺLocations‐Users‐Itemsʺ.

Spyros Panagiotakis defined context as various relevant information with user, including the network used by user, the userʹs location information, environmental information around the user, and so on.

Similar to the userʹs interest model, when building the context model, we must also take those factors related with location into account as far as possible, which may affect the userʹs behaviors, to improve the modelʹs richness of information, such as geographic latitude and longitude, time, weather, mobile terminalʹs information and network circumstances. For the same reason of the userʹs interest model, the context model should be updated in time to capture the change of interests for more personalized results.

For example, using collaborative filtering recommen‐ dation system, we can use userʹs imformation and historical behaviors to establish the interest model, and use geographical location, environmental conditions,

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device and network status information to create the context model. Then according to the collaborative filtering algorithm, we find the userʹs neighbors from a lot of people, and obtain recommended results, as shown in Figure 2.

FIG. 2 COLLABORATIVE FILTERING RECOMMENDATION ALGORITHM BASED ON LBS

Optimization Algorithm to Solve the Cold Start Problem Among all the current recommendation systems, the widely‐used algorithms include content‐based recommendation algorithms, collaborative filtering recommendation algorithm, rule‐based recommendation algorithm, knowledge‐based recommendation algorithms and statistics‐based recommendation algorithm; of which the content‐based recommendation algorithm is the simplest one in terms of implementation. But it produces the recommended results only based on the similarity between items, so it is not easy to find the userʹs new points of interest. While Rule‐based, knowledge‐based and statistics‐based algorithm have better recommendation effect, but they need to collect and extract a large amount of data, and the computational processes are complex. They are not suitable for applying on mobile terminals, which would bring the huge challenge to terminalʹs battery life.

The collaborative filtering recommendation algorithm is still the most widely used algorithm which produces commendation according to the userʹs behaviors. It can handle complex items well, and find the userʹs new points of interest. But this algorithm suffers the problems of sparse data and ʺcold startʺ.

The ʺcold startʺ problem can be divided into two issues, new items and new users. The former is due to a new item lack of adequate evaluation, leading to this items difficult to be recommended; while the latter is due to a new user lack of evaluation date for any item, so the recommendation system cannot know his/her interest.

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For the new items issue, many scholars have proposed the combination algorithm of content‐based and collaborative filtering. That is, the neighbor itemsʹ average score is utilized to be the new item’s predicted score; or the score prediction is calculated by using the weighted value of neighbor items.

For the new users issue, we can present those items to him/her which are the most popular items while using the system. But this is only an expedient measure, because popularity doesnʹt mean personalization. In other ways, when a person uses the system for the first time, the system asks the user to make evaluation with some outstanding representative items, in order to acquire the userʹs points of interest. Meanwhile, system should analyse users’ interest timely and update their interest models for personalized recommendation, when the system can capture their habits.

Conclusion With the continuous increase of market share and utilization, more and more mobile devices are introduced through LBS technologies. Many existing softwares such as Foursquare, jiepang and sina microblog, are good social service software based on LBS. Accordingly, taking locational information into account is an inevitable trend for recommendation system. Such recommendation systems are bound to provide a better personalized recommended experience.

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ZHAO Shou‐xiang served as the director of Department of Management Science and Engineering. She is a member of the expert group from China Association for Bar Code Technology and Application, and Distinguished experts in electronic commerce of Beijing Vocational College of Agriculture. She has participated in several national and municipal projects, published more than 20 papers, and edited ʺNetwork Economicsʺ, ʺE‐commerceʺ, ʺEnterprise Informatizationʺ and other materials.

WANG Chen, graduated from Beijing Technology and Business University, and he is currently studying as graduate student in management science and engineering in this school. Following the research supervisor Zhao Shou‐xiang, his research interests are the personalized recommendation system in e‐commerce.

Zibuschka J, Rannenberg K, Kölsch T. Location‐based services[M]//Digital privacy. Springer Berlin Heidelberg, 2011: 679‐695.

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