SAPRS: Situation-Aware Proactive Recommender System with Explanations

Punam Bedi1, Sumit Kumar Agarwal2, Samarth Sharma3, Harshita Joshi4 Department of Computer Science

University of Delhi New Delhi, India

1punambedi@ieee.org, 2sumitsagarwal@rediffmail.com, 3samarth.mcs.du.2012@gmail.com, 4harshita.mcs.du.2012@gmail.com

Abstract— Proactive recommender systems are smart applications which deliver the recommendations on users’ mobile devices automatically, without their intervention. Such systems help the users in timely reception of the information of their interest. Improving user’s acceptance on pushed recommendations of these systems is a challenging task. In these systems, determining right push context (situation) and finding relevant items for the target user are considered as two vital issues for achieving better user acceptance. Moreover, along with the pushed recommendations, if the target user is also shown the explanation why something is recommended to him then this transparency might help the user to make a better decision & increase his faith in the pushed recommendations for improving user’s acceptance. Therefore, we present a Situation-Aware Proactive Recommender System (SAPRS) that pushes both relevant and justifiable recommendations to the target user at the right context only in order to achieve better user acceptance. SAPRS works in two phases; (i) situation assessment phase and the (ii) item assessment phase. In situation assessment phase, the proposed system analyzes the current situation i.e. whether or not the current context needs a recommendation to be pushed. In the Item assessment phase, SAPRS generates relevant recommendations for the target user using a location-aware reputation based collaborative filtering algorithm. It also enhances the transparency of the pushed recommendations by means of explanations in this phase. The prototype of SAPRS is implemented using multi-agent approach for restaurant recommendations and its performance is evaluated using precision, recall metrics and feature based comparisons.

Keywords— Recommender Systems; Pro-activity; Situation-Awareness; Explanation; Multi-Agent System; Reputation

1. INTRODUCTION With the explosive growth of information and online

services, it becomes more and more difficult for the users to find the required information to complete a specific task. Recommender systems are widely used intelligent applications, which help the users to deal with this information overload problem by providing personalized recommendations [9, 11]. Conventional recommender systems typically follow a pull model i.e. these systems give suggestions only when a user makes an explicit request. However, in some application domains (e.g., the problem of suggesting a good restaurant to a target user when he is moving from one place to other), the availability of items changes often and rapidly. In such application domains, the pull model seems less effective in

helping users keep track of their interested items [10] because at the time of user’s request, suitable items may not be available, but when they will become available (often in short time) the user may not know. The applicability of pull model may become worse in mobile recommender systems for such application domains, due to the limitations of mobile devices such as small display size or missing keyboard, data input and information browsing is inconvenient [34].

The proactive recommender systems discussed in the literature are kind of mobile recommender systems [19], which help the users in the timely reception of the information of their interest. Such systems aim to reduce interaction for achieving better user experience in mobile environment by pushing relevant recommendations to the user at right situation [4]. As the user does not request for items in proactive recommender systems, improving user’s acceptance of pushed recommendations of these systems is a challenging task. In these systems, determining right push context (situation) and finding relevant items for the target user are taken as two crucial issues. This is due to the reason that if such systems push uninteresting information to the user, or even push interesting information to the user at inappropriate context, then chances of user accepting the pushed information will be less [8]. Moreover, to increase the acceptability and transparency of pushed recommendations, it is important to explain the relevance of the items recommended to a user so that he can make an efficient choice(s) from the options presented before him. Therefore, we propose a Situation-Aware Proactive Recommender System (SAPRS) that not only pushes the relevant recommendations to the user at the right context, but it also gives the explanation to the user why a particular item is recommended to him in order to achieve better user acceptance. In the proposed system SAPRS, each user registered with it is represented by an agent. It works in two phases: (i) situation assessment and the (ii) item assessment phase. In situation assessment phase, it determines whether or not current context needs a recommendation to be pushed using fuzzy logic. In item assessment phase, it finds the relevant items for the target user using location-aware reputation based collaborative filtering algorithm. It also generates an explanation about the pushed recommendations in the second phase using argumentation approach.

The rest of the paper is organized as follows. Section 2 discusses the work carried out by other researchers in this field.

277978-1-4799-3080-7/14/$31.00 c©2014 IEEE

The recommendation generation and explaSAPRS is presented in section 3. Experimenshown in section 4. Finally, conclusion andgiven in section 5.

2. RELATED WORK Research in mobile recommender syste

predominately focused on designing better utilizing more and more contextual informarecommendation process, to produce recommendations for the mobile users [19, recent survey on electronic mobile guides, Kediscussed that nowadays pro-activity has attention in personalization and recommender A survey on mobile recommender systemsconferred that various systems make use obehavior with contextual information personalization in mobile devices. The accomplished in [13, 27] presented the three mof proactive recommender system: (a) information: the right information to the righttime, (b) Long Term Memory: what the ususing it, (c) Unobtrusiveness: avoid disturbinguser.

The approaches presented in [1, 2, 18, 23] the user that are situated near his current approaches do not consider user’s prerecommendation generation. The systems devepush items to users based on their prefeconcerning the right push context. Bedi andpresented trust model to improve personalizaYeung and Yang [35] proposed AHP model thnetwork to predict user’s interest that accounproactive personalized recommendations. Bproposed rule based framework of context-awpush service to enhance the overall intentioinformation systems in tourism domain. Ngu[29] presented push delivery recommendationprovide proactively relevant recommendationthe right context.

Bouzeghoub et al. [14] presented architectaware adaptive recommendation generatioenvironment depending on dynamic evolutioni.e. his current activity, position and profile.proposed architecture for context-awrecommender system based on reduction-baset al. [4] presented situation awareness model past and future situations to determine the riusing fuzzy logic for pushing recommendatio[5] presented an argumentation based explanatimproving transparent behavior of proactivsystem in order to achieve better user accepta[22] proposed an agent-based model for proacservices based on context history. This model erecommendations based on user’s preferededuced from the context history.

Although the considerable amount of woron proactive recommender systems, the solutiuser’s acceptance in such systems is still a

anation process of ntation details are d future work are

ems to date has algorithms and

ation within their more relevant 26, 30]. In the

enteris et al. [24] gained a large system research.

s by Ricci [30] of current user’s

to improve research work

main requirements Relevance of

t user at the right er has done and and irritating the

push all items to location. These

eferences during eloped in [16, 25] ferences, without d Agarwal [6, 7] ation in mobiles. hat uses Bayesian nts for delivering Beer et al [12] ware information

on to use mobile uyen and Hoang n methodology to ns to the user at

ture for situation-on in a mobile n of user situation . Tair et al. [33]

ware proactive ed theory. Bader that incorporates

ight push context ons. Bader et al. tion approach for ve recommender ance. Hong et al. ctive personalized enables proactive nces which are

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We propose a novel approach SAPRand justifiable recommendations to context only in order to achieve bett

3. PROPOSED SITUATIONRECOMMENDER

In this section, we present recommender system (SAPRS) thapushing the relevant and recommendations to the mobile useframework of SAPRS systemgeneration process of SAPRS is disc

3.1. Framework of SAPRS SAPRS is a multi-agent system

represented by an agent. The multi-SAPRS has been chosen because uthe workload can be distributed aagents may work on behalf of usersimultaneously for increasing the efbasic building blocks of SAPRS are

Fig. 1. Framework of S

On the client side, an androithrough which the mobile user canWhenever a mobile user registers component, a corresponding user agThe basic functionality of user ainformation about target mobilecoordinates with other componensituation assessment module, contrecommendation engine and explarelevant and justifiable recommendcontext information collector coperiodically detects the information as his current location and time iestimate of contextual attributes (situation assessment module determ(situation) for recommendationsrecommendation engine maintainsgenerates the recommendations explanation engine generates the exitems for the user agent. Figure 1SAPRS system.

RS that pushes both relevant the target user at the right

ter user acceptance.

N-AWARE PROACTIVE SYSTEM situation-aware proactive

at automates the process of justifiable restaurant

ers. Section 3.1 presents the m. The recommendation

cussed in section 3.2.

m in which every user is -agent based framework for using multi-agent approach among the agents and the rs to perform assigned task fficiency of the system. The shown in figure 1.

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id application is installed n interact with the system. with the system using this

gent is created at the server. gent is to infer and keep e user. The user agent nts of the server such as text information collector,

anation engine for pushing ations to a target user. The omponent of the server about the mobile user such

in order to obtain a good (distance, time etc). The

mines the right push context s to be pushed. The s the access database and for the user agent. The

xplanation about the pushed 1 shows the framework of

278 2014 International Conference on Advances in Computing,Communications and Informatics (ICACCI)

3.2. Functioning of SAPRS The proposed system SAPRS works in two phases: (i)

situation assessment phase and (ii) item assessment phase. The situation assessment phase is executed periodically in the background to determine the right push context. The item assessment phase estimates the relevant items as recommendations to be pushed to the target user. In item assessment phase, the explanation for the target user about the pushed items is also generated to improve his acceptance. This phase starts only when the situation assessment phase signifies a promising situation.

3.2.1. Phase 1: Situation Assessment In this phase, the system needs to determine right context

when the recommendations must be pushed to the target user. To do so, the system computes a context level using fuzzy logic. Fuzzy logic is a multi-valued logic that allows intermediate values to be defined between conventional evaluations like yes/no, true/false etc [37]. It is a superset of conventional (Boolean) logic that has been extended to handle the uncertainty. It allows smooth transitions between values in fuzzy set without abrupt behavior, which should be avoided in proactive recommender systems. Determining right push context is an inherently uncertain process in proactive recommender systems, which cannot be adequately dealt with binary logic or crisp logic. Therefore, fuzzy logic has been used in SAPRS to handle uncertainty during context level computation within situation assessment phase. The fuzzy logic also allows transparent behavior of the system by using linguistic variables, which accounts for the acceptance of a proactive recommender system [5].

The context level is a value between 0 and 1. It is calculated based on three contextual attributes such as distance, time and budget. These contextual attributes are used as linguistic input variables within the system. The values for these input variables are mapped by the system as fuzzy number by using suitable fuzzy sets. The considered criteria, rules defined for mapping of input variables to output variable and the overall context level calculation are presented in [10] in detail. If the computed context level value exceeds some threshold, then second phase will be initiated. Otherwise, situation assessment phase is executed again after some time period.

3.2.2. Phase 2:Item Assessment Item assessment phase is further divided into two

processes: (i) recommendation process and the (ii) explanation process. In recommendation process, SAPRS determines the relevant items as recommendations using location-aware reputation based collaborative filtering (LRCF) algorithm. In explanation process, SAPRS generates justification why a particular item recommendation is given to the user. The recommendation process and the explanation process within item assessment phase are described in detail below:

A. Recommendation Process: In this process, SAPRS evaluates the relevant items to be

recommended for the target user using LRCF algorithm. This algorithm deals with a set of geographically located recommendable items, A= { I1, I2, I1, I2,…… In } based on the

parameter Router that establishes the limit on the farthest item that could be recommended. This parameter, Router is used to compute a subset, A’⊆ A, that includes those items that are suitable to be recommended to the user according to his location and ignoring the remaining ones because they are far away from user’s location. LRCF algorithm works in two processes: offline process and the online process. In offline process, the system agents are executed in the background to periodically compute and store the required information such as reputation of each item and similarity between users. This information is used in online process during recommendation generation for the target user. The offline process and online process of LRCF algorithm are presented in [8] in detail.

B. Explanation Process SAPRS system generates explanation about the pushed

items using argumentation approach. An argument [15] is a statement containing a piece of information related to the aspect which should be explained , e.g., "The recommended restaurant is inexpensive" or "The recommended restaurant servers only vegan food" etc. The explanation argument within SAPRS depends on following four explanation factors:

• Affordability (A): Affordability of user ‘u’ for restaurant ‘i’ is computed as the multiplicity of meals that he can purchase at restaurant ‘i’ in his given budget category. It is computed using following formula in the system:

Affordability, A(u,i) = Upper-Limit-BC(u) / Per-person-Cost(i) (1)

Where,

Upper-Limit-BC(u) represents the upper limit of user u in his budget category

Per-person-Cost(i) represents per person food cost at restaurant i.

• Cuisine-Preference (C) : Cuisine-Preference(u, i) is the proportion of favorite cuisines of the user ‘u’ being served at restaurant ‘i’. For example, the favorite cuisines of user u are {North-Indian, South-Indian, Continental} and the cuisines served at restaurant ‘i’ are {South-Indian, Continental, Mughlai}, then the cuisine-preference (u, i) will be 2/3.

• Vegan-Factor (V): Vegan-factor accounts for the reluctance of a strictly vegan-user to dine at a restaurant which serves non-veg food.

Vegan-factor (u,i) = Wvf, if u is strictly-vegan and restaurant ‘i’ serves non-veg.

1, otherwise (2)

Where, Wvf is a statistically determined constant from users’ survey (see Table 1).

• Reputation of Item (R): The reputation of restaurant in the system depends on three factors: average rating of the restaurant, number of users who rated that restaurant and how close the rating for that restaurant provided by the users is, to each other. The reputation of restaurant ‘j’ (ROIj) in the system is computed as:

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(3)

Where,

avgj represents the average rating of jth restaurant

nj represents number of users who rated jth restaurant

N denotes total number of users.

SDj denotes standard deviation of the ratings given by individual users for the jth restaurant. A small SDj indicates that the ratings are around the mean i.e. ratings of the users are close to each other.

The explanation argument is generated by computing explanation-score and information-score in the system. The explanation-score describes the performance of explanation factors (A, C, V and R) and the information-score measures the amount of information in an explanation factor relative to the current recommendation-set. The explanation-score is computed by determining maximum contributing explanation factor as below:

Explanation-score = MAX(Wa × A, Wc × C, V, Wr × R) (4)

Where,

Wa, Wc and Wr are statistically determined constants from users’ survey (see Table 1).

Using explanation-score alone, there is a possibility that only a single-factor is chosen for most of the items. In that case, each of recommended items would be explained only on that factor. This drastically reduces a user’s ability to distinguish between different recommended items based on their explanations. To handle this situation, an information-score is computed for each explanation factor (A, C, V and R) as below:

Information-score (F) = (R + I)/2 (5)

Where,

F represents explanation factor (A, C, V or R)

R = (Maximum value of explanation factor in the pushed recommendation list – Minimum value of explanation factor in the pushed recommendation list)

I represents Shannon’s entropy

To ease this discussion, suppose maximum contributing factor for item ‘i’ was Affordability and If Information-Score (Affordability) < threshold, then Affordability alone is not sufficient to explain this item and a supplementary argument is chosen through the same procedure.

After deciding the explanation factors for generating explanation argument based on explanation-score and the information-score, the system maps the computed values of selected explanation factors into linguistic terms. For example, If Affordability is the best performing factor for a restaurant, then; Explanation-Score is mapped to Expensive, Affordable or Inexpensive as follows:

• Linguistic term = “Expensive”, if Affordability <= 1- delta

• Linguistic term = “Affordability”, if 1-delta <Affordability <= 1+delta

• Linguistic term = “Inexpensive”, if Affordability > 1+ delta

Delta is a controlling parameter which captures the degree of indifference of a user to spend slightly over or below his budget.

4. EXPERIMENTAL DETAILS The prototype of SAPRS was implemented using JADE

(Java Agent Development Environment) for creating multi-agent environment, Eclipse IDE with Android SDK for creating user interface, MySQL 5.5 for backend database, Apache Web Server 2.2 for running our server side components, PHP 3.0 for sending and receiving web request between Android App and Apache Web Server and jFuzzyLogic library to develop situation assessment module of the system.

To prepare a dataset for the experiment, we collected the information of restaurants of Delhi such as restaurants name, their opening and closing time, food type, average food cost per person, and their addresses from http://www.zomato.com/ncr/restaurants website and stored into local database. Then we retrieved and stored the longitude and latitude of these restaurants into local database to build a restaurant dataset by using reverse geo-coding tool available at http://www.distancesfrom.com/latitude-longitude.aspx. The prepared restaurant dataset contains the information of 2166 restaurants of Delhi, India.

Online users’ subjective feedback was taken to determine the importance of each explanation factor. To do so, 107 users of diverse food interests’ of different age groups belonging to 5 different locations of Delhi were requested, out of which only 83 users gave their feedbacks. The mandatory questions, which were asked during this feedback, are shown in Table 1.

The performance of SAPRS was evaluated using precision, recall and F1 metrics. Precision is a measure of accuracy or fidelity and recall or sensitivity is a measure of completeness [20]. Precision score of 1.0 signifies that all recommendations retrieved were relevant. Recall score of 1.0 signifies that all relevant recommendations were retrieved. The F1 measure consists of weighted combination of precision and recall. One of the ways to evaluate precision and recall is to predict the top N items for recommendation. To evaluate the goodness of recommendations in SAPRS, the produced recommendations were pushed to all registered users and their feedbacks were considered for evaluation. The pushed recommendation list to the target user and his corresponding feedback are shown in figure 2. The precision, recall and F1 measures are computed on restaurant dataset by varying the values of top N from 3 to 10. The results obtained from the experimentations are shown in figure 3. These results illustrate that as top N increases, recall increases and precision decreases. The performance of SAPRS was also compared with conventional CF (Collaborative filtering) based and LRCF based (without explanation) prototypes. The results obtained from these experimentations are shown in figure 4. Results from figure 4 illustrates that SAPRS with explanation gives better performance than conventional CF and LRCF (without

280 2014 International Conference on Advances in Computing,Communications and Informatics (ICACCI)

explanation). The feature based comparison between SAPRS and existing mobile recommender systems was also done as depicted in Table 2.

TABLE I. AN ONLINE USERS’ SUBJECTIVE FEEDBACK

1. How often do you eat out?* i. Quite often ii. Once a month iii. Occasionally iv. Rarely

2. When visiting a restaurant, which criteria are important for you? (Kindly rate on a scale of 1-5, 5 being the highest rating)* i. Ambiance ii. Food quality iii. Service iv. Price v. Reachability vi. Cuisine

3. For which meal do you prefer to visit a restaurant? (Choose all applicable)* i. Breakfast ii. Lunch iii. Dinner

4. How far is your favorite restaurant from your home?* i. Less than 1 km ii. 1-5 km iii. More than 5 km

5. Would you deviate from your route while driving to have lunch/dinner at a good restaurant that a friend has recommended?* i. Yes ii. No iii. Can’t say

6. How much on an average do you spend on a meal at a restaurant?* i. Below 250 ii. 250-500 iii. 500-750 iv. Above 750

7. Which of the following is true for you?* i. Are you strictly a vegetarian? ii. Are you a vegetarian and an eggetarian?

8. Would you prefer to dine at a restaurant that serves both vegetarian as well as non-vegetarian food (meat, fish, ham etc.)?* (this question is put forth only if option i is checked in the previous question) i. Yes ii. No

Fig. 2. Depicts pushed recommendation list to the target user and his

corresponding feedback.

Fig. 3. Effect of Top N on Precision and Recall

Top N =3 Top N=5 Top N = 10

Precision 86.29 73.11 49.23

Recall 12.29 17.87 24.23

F1 21.52 28.72 32.48

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Fig. 4. Comparision between SAPRS with Conventional CF and LRCF

TABLE II. FEATURE BASED COMPARISON BETWEEN SAPRS AND EXISTING MOBILE RECOMMENDER SYSTEMS

5. CONCLUSION AND FUTURE WORK A Situation-Aware Proactive Recommender System (SAPRS) has been designed and developed for recommending relevant and justifiable restaurants for the mobile users for achieving better users’ acceptance. The performance of SAPRS was evaluated and compared with conventional CF and LRCF approach. Experimental results of our approach show significant improvement over conventional CF and LRCF approaches. The feature based comparison between SAPRS and existing mobile recommender systems has also done in this work, which shows that SAPRS provides more features over the existing mobile recommender systems.

As a future work, we will be working towards the explanation interface of proactive recommender system in order to improve user’s acceptance.

ACKNOWLEDGEMENTS

The authors would like to thank University Grants Commission (UGC) of India for supporting this research work as part of the Major Research Project “Trustworthy Proactive Recommender System” to Dr. Punam Bedi as PI.

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