sensors Article
An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment Lianyong Qi 1, *, Shunmei Meng 2,3 , Xuyun Zhang 4 Zhili Zhou 6 and Wanchun Dou 3 1 2 3 4 5 6
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ID
, Ruili Wang 5 , Xiaolong Xu 6
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School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China;
[email protected] State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China;
[email protected] Department of Electrical and Computer Engineering, University of Auckland, Auckland 1023, New Zealand;
[email protected] Institute of Natural and Mathematical Sciences, Massey University, Auckland 0745, New Zealand;
[email protected] School of Computer and Software, Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China;
[email protected] (X.X.);
[email protected] (Z.Z.) Correspondence:
[email protected]; Tel.: +86-0633-3981060
Received: 23 May 2018; Accepted: 22 June 2018; Published: 26 June 2018
Abstract: Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach. Keywords: service recommendation; privacy-preservation; failure; exception handling; converse Locality-Sensitive Hashing
1. Introduction With the advent of Web of Things (WoT), an increasing number of enterprises or organizations are apt to encapsulate their products (e.g., web API (Application Programming Interface)) into easy-to-access web services and publish them on the web so as to attract potential users and gain more profits. However, the ever-increasing volume and varieties of candidate services place a heavy burden on the service selection decisions of target users [1]. Under the circumstance, Collaborative Filtering (CF)-based recommendation techniques are proposed to minimize such burdens. Typically, for a target
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for a target user who requires recommend services, the recommender system can look for his/her similar friends by recommend observing the qualitythe of service (QoS) data monitored and then user who requires services, recommender system can look by forvarious his/hersensors, similar friends by enact appropriate recommendation with by thevarious help ofsensors, derived friends. Nowadays, CF observing the quality of service (QoS) decisions data monitored and then enact appropriate technique has beendecisions successfully applied in many recommender whose data recommendation with the help of derived friends. systems Nowadays, CFdecision-making technique has been for recommendation are organized or stored in a centralized way. successfully applied in many recommender systems whose decision-making data for recommendation However,orinstored the in cloud computing are organized a centralized way.environment, the QoS information that is crucial to recommendation decisions is often not centralized, but rather by distributed However, in the cloud computing environment, the monitored QoS information that issensors crucialand to stored in different decisions cloud platforms In centralized, this situation, it is necessary for a recommender system to recommendation is often[2]. not but rather monitored by distributed sensors integrate orinfuse these cloud distributed data[2]. across different cloud and properly, so as and stored different platforms In this situation, it isplatforms necessaryquickly for a recommender system to make comprehensive and accurate recommendation decisions. In particular, to protect to integrate or fuse these distributed data across different cloud platforms quickly and properly, sothe as sensitive business information and recommendation obey the laws decisions. [3–5], preserving usertoprivacy during the to make comprehensive and accurate In particular, protect the sensitive abovementioned multi-source integration process is an important but challenging task [6–10] business information and obeydata the laws [3–5], preserving user privacy during the abovementioned for the success of subsequent recommendations. multi-source data integration process is an important but challenging task [6–10] for the success of The Locality-Sensitive Hashing (LSH) technique [11] has recently been recruited to make subsequent recommendations. efficient and privacy-preserving service(LSH) recommendation in the environment. The Locality-Sensitive Hashing technique [11] hasdistributed recently been recruitedTypically, to make according to privacy-preserving the QoS data, the Locality-Sensitive Hashingintechnique can beenvironment. used to search for the efficient and service recommendation the distributed Typically, similar friends of a target user in an efficient and privacy-preserving manner. Afterwards, according to the QoS data, the Locality-Sensitive Hashing technique can be used to search for recommended results generated byanconsidering theprivacy-preserving preferences of obtained similar friends. the similar friends of are a target user in efficient and manner. Afterwards, However, in certain situations, the recommender system generate producesimilar any satisfying recommended results are generated by considering thecannot preferences of or obtained friends. recommended result; in other words, a recommendation failure occurs. While existing LSH-based However, in certain situations, the recommender system cannot generate or produce any satisfying service recommendation approaches consider this kind of occurs. recommendation failureLSH-based problems recommended result; in other words,seldom a recommendation failure While existing as wellrecommendation as the corresponding exception handlingthis solutions; therefore, the robustness of the service approaches seldom consider kind of recommendation failure problems as recommender system is reduced significantly. well as the corresponding exception handling solutions; therefore, the robustness of the recommender Anisintuitive is presented in Figure 1, which contains three users and six services. The system reducedexample significantly. user An ratings are denoted by 1*–5*. According traditional theservices. index values of intuitive example is presented in Figureto 1, the which contains LSH threetechnique, users and six The user Tom and Alice are not same as they have no co-invoked services. Therefore, Tom is not similar with ratings are denoted by 1*–5*. According to the traditional LSH technique, the index values of Tom Alice. Likewise, Figure shows, is not similar with Bob either. InTom thisissituation, no with satisfying and Alice are notassame as 1they haveTom no co-invoked services. Therefore, not similar Alice. candidate services can be recommended or returned to Tom, i.e., the service recommendation process Likewise, as Figure 1 shows, Tom is not similar with Bob either. In this situation, no satisfying candidate is failed. can be recommended or returned to Tom, i.e., the service recommendation process is failed. services
Figure Figure 1. 1. A A recommendation recommendation failure failure example (see our previous work [12]).
In view of this challenge, we propose converse LSH technique and utilize it to look for a target In view of this challenge, we propose converse LSH technique and utilize it to look for a target user’s contrary users (denoted by “enemy” in this paper) whose preferences are totally different from user’s contrary users (denoted by “enemy” in this paper) whose preferences are totally different from the target user. Afterwards, according to the enemies of the target user, we infer the possible friends the target user. Afterwards, according to the enemies of the target user, we infer the possible friends of of the target user indirectly so as to handle the exception incurred by recommendation failures. the target user indirectly so as to handle the exception incurred by recommendation failures. Overall, Overall, the contributions in this paper are as follows: the contributions in this paper are as follows: (1) A novel LSH variant named converse LSH is developed, which can be utilized to search for the (1) A novel LSH variant named converse LSH is developed, which can be utilized to search for the enemy users of a target user, in a time-efficient and privacy-preserving way. enemy users of a target user, in a time-efficient and privacy-preserving way. (2) We utilize converse LSH technique to search for the enemies of a target user and then look for the target user’s similar friends indirectly based on the “enemy’s enemy is a possible friend”
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We utilize converse LSH technique to search for the enemies of a target user and then look for the target user’s similar friends indirectly based on the “enemy’s enemy is a possible friend” inference rule in Social Balance Theory. Afterwards, we generate recommended results by considering the preferences of obtained similar friends, so as to handle the exceptions incurred by recommendation failures. Comprehensive experiments are simulated based on Movielens dataset, to test the effectiveness of suggested recommendation approach. Experiment results indicate the advantages of our proposal compared to other competitive approaches when a recommendation failure occurs.
The rest of this paper is structured as follows: in Section 2, we introduce the related work. Converse LSH technique is proposed in Section 3. An exception handling approach based on converse LSH is put forward in Section 4, to achieve indirect friend finding and service recommendations. Experiment evaluations are given in Section 5. In Section 6, we summarize the paper and point out the future research directions. 2. Related Work Many researchers have investigated the privacy concerns in recommendation process and provide their respective resolutions. In [13], the authors suggested that a user can publish partial QoS data to the service community, so as to protect the remaining majority of QoS data. Similarly, in [14], the authors take the amount of published data as a tunable parameter and then transform the privacy-preservation problem into a multi-object optimization problem, so as to achieve a good tradeoff between data availability and data privacy. However, in the above approaches, certain sensitive information about users may be in danger due to the published partial data. Besides, recommendation failures are not considered in these approaches. Microaggregation idea is adopted in [15] to realize data K-anonymization so that the users’ sensitive data (e.g., user location) can be protected. However, there is often a tradeoff between data availability and data privacy; so the recommendation accuracy is often not as high as expected if the anonymous data are employed to make service recommendation decisions. Besides, these approaches do not discuss the possible service recommendation failures. Encryption technique is adopted in work [16] to guarantee the privacy-preservation of sensitive information. However, as a heavy-weight privacy-preservation manner, encryption operations often lead to high computational cost and long delay; therefore, the encryption techniques are often not applicable to the light-weight service recommendation requirements from certain users. Besides, recommendation failures are out of the scope of these encryption-based approaches. Randomized disturbance idea is adopted in [17] to convert the real QoS data into the disturbed data; afterwards, the latter data are regarded as the recommendation bases to achieve the privacy-preservation goal. However, recommendation failures are not discussed; besides, the applicability of this approach is relatively limited as it can only be applied to the Pearson Correlation Coefficient (PCC)-based collaborative service recommendation scenarios. In [18], the authors utilize the Differential Privacy technique to make noise data injection and confusion, so as to ensure that the real service quality data would not be exposed to the outside. However, the time complexity of Differential Privacy is relatively high; second, when the service quality data are updated frequently, the accumulated noise would be enlarged, which will decrease the service recommendation accuracy accordingly; third, they do not consider the recommendation failures. As an effective and efficient way to search for similar friends in the big data context, LSH is recently introduced into service recommendation to achieve the distributed data integration and privacy-preservation goals. In our previous work [19–21], LSH is combined with user-based CF to make privacy-preserving service recommendation. Likewise, in [22], LSH is combined with item-based CF to build service index table with little privacy and then make service recommendation based on the service index table. However, these LSH-based recommendation approaches do not consider the recommendation failures as well as the corresponding exception handling resolutions. As to the
rough the above literature review, a conclusion be drawn that existing recommendatio ches either fail to protect user privacy or overlookcan the recommendation failures and exceptio ches either fail to protect user privacy or overlook the recommendation failures of this drawback, we propose converse LSH technique and utilize it to handleand the exceptio exceptio of this drawback, we propose converse LSH technique and utilize it to handle the exceptio d by recommendation failures; this way, the robustness of recommender4 ofsystems can b Sensors 2018, 18, 2037 11 d by recommendation failures; this way, the robustness of recommender systems can b ed significantly. ed significantly. recommendation failures, the authors in [23] adopt the average idea to predict the missing QoS data.
Social Balance Theory is utilized in our previous works [12,24] to look for the possible friends of a target erse Locality-Sensitive Hashing user so as to cope with the recommendation failures. However, privacy concerns are not discussed. erse Locality-Sensitive Hashing Through the above literature review, a conclusion can be drawn that existing recommendation aditional LSH is an effective similar neighbor search technique. Therefore, in the servi approaches either fail to protect user privacy or overlook the recommendation failures and exceptions. aditional LSH is an LSH effective similar neighbortechnique search technique. Therefore, in the servi mendation domain, is often integrated user-based In view of this drawback, we propose converse LSHwith and utilize it toCollaborative handle the exceptionsFiltering (C mendation isfriends oftenfailures; integrated with user-based Collaborative incurred byLSH recommendation this way, the robustness of recommender systems can beFiltering (C ue to searchdomain, for the similar of a target user, in an efficient and privacy-preserving wa improved significantly. ue to search for the similar friends of a target user, in an efficient and privacy-preserving wa section, we modify the traditional LSH technique and transform it into converse LSH whic 3. Conversethe Locality-Sensitive Hashing technique and transform it into converse LSH whic section, modify traditional used to we search for the enemies of LSH a target user efficiently while guaranteeing user privac Traditional LSH is an effective similar neighbor search technique. Therefore, in the service user privac used to search for the enemies of a target user efficiently while guaranteeing e introduce recommendation the rationaledomain, of converse LSH. LSH is often integrated with user-based Collaborative Filtering (CF) e introduce the rationale of converse LSH. technique to search for the friends a target an efficient privacy-preserving 1, …,user, 1, …, t’s consider two n-dimensional similar vectors X =of (x xn)inand Y = (yand yn) whose similarity ca way. In this section, we modify the traditional LSH technique and transform it intoyconverse LSH similarity ca 1, …, xn) and Y = (y1, …, n) whose t’s consider two n-dimensional vectors X = (x cted by the PCC distance. Next, according to LSH theory [11], we can transform which can be used to search for the enemies of a target user efficiently while guaranteeing user privacy.vectors X an cted the PCC distance. Next, according to LSH theory [11],with we can transform X an Next, we introduce the rationale of converse LSH. iningbyprivate information into corresponding hash values little privacy, vectors i.e., h(X) an Let’s consider two n-dimensional vectors X = (x , . . . , x ) and Y = (y , . . . , y ) whose similarity n n 1 1 ining private information into corresponding valuesVis with little privacy, vector i.e., h(X) spectively. Concretely, be calculated byhash (1), where ancan n-dimensional (v1,an … can be depicted h(X) by the can PCC distance. Next, according to LSH theory [11], we transform vectors spectively. Concretely, h(X) can be calculated by Viswith anthe n-dimensional (v1, … Xn) and containing private information into[−1, corresponding hash values little privacy, product i.e., h(X) vector vj (j = 1, 2, … isYrandomly selected from 1];(1), “ ”where represents inner of differe respectively. Concretely, can be calculated (1), where V is anthe n-dimensional vector j (j = 1, 2, …and vThe n) h(Y), is randomly selectedh(X) from 1]; “ by” V represents inner product of differe physical equation in (1)[−1, is: vector is“◦a” represents hyper plane (v1 , .meaning . . , vn ) and vj of (j = 1, 2, . . . n) is randomly selected from [−1, 1]; the inner which product ofdivides the The space physical meaning of physical equation (1) is: vector V is aV V plane different vectors. The meaning equation in (1) is: plane vector ishyper a(i.e., hyper X○V plane which ional into two parts; if point X in is ofabove hyper > which 0),divides thendivides h(X) = 1the wi the n-dimensional space into two parts; if point X is above hyper plane V (i.e., X#V > 0), then h(X) = 1 ional space into two parts; X is above hyper plane V (i.e., X○V > 0), then h(X) = 1 wi obability; otherwise, h(X) =if 0:point h(X) with high probability; otherwise, = 0: obability; otherwise, h(X) = 0: ( 1if ifXX V 1 V >0 0 (1) h X h(X)1= 0if ifXX VV ≤0 0 ( if X V 0 h X 0 ( if X V 0 X and Y are transformed into two Thus, through the hash map in (1), 0 n-dimensional vectors values,map i.e., h(X) respectively. However, LSH is X essentially us, through Boolean the hash in and (1),h(Y), n-dimensional vectors and Ya probability-based are transformed into tw similar friend search technique; therefore, a single hash value h(X) or h(Y) cannot precisely represents through the hash map (1), n-dimensional vectors Xisand Y are transformed into tw nus, values, i.e.,the h(X) and h(Y),inrespectively. However, LSH essentially probability-base original n-dimensional vector X or Y. Considering this, more hash functions, i.e., a hash a function nfriend values, i.e.,family h(X) h(Y), respectively. However, LSH isfunction essentially a we probability-base H(.)and = {h1 (.), . . . , hr (.)} (r
