Where Do Local Experts Go? Evaluating User Geo-Topical Similarity for Top-<Emphasis Type="Italic">N</Emphasis> Place Recommendation

Recent years have witnessed the ever-growing popularity of location-based social network (LBSN) services. Top-N place recommendation, which aims at retrieving N most appealing places for a target user, has thus gained increasing importance. Yet existing solutions to this problem either provide non-personalized recommendations by selecting nearby popular places, or resort to collaborative filtering (CF) by treating each place as an independent item, overlooking the geographical and semantic correlations among places. In this paper, we propose GoTo, a collaborative recommender that provides top-N personalized place recommendation in LBSNs. Compared with existing methods, GoTo achieves its effectiveness by exploiting the wisdom of the so-called local experts, namely those who are geographically close and have similar preferences with regard to a certain user. At the core of GoTo lies a novel user similarity measure called geo-topical similarity, which combines geographical and semantic correlations among places for discovering local experts. In specific, the geo-topical similarity uses Gaussian mixtures to model users’ real-life geographical patterns, and extracts users’ topical preferences from the attached tags of historically visited places. Extensive experiments on real LBSN datasets show that compared with baseline methods, GoTo can improve the performance of top-N place recommendation by up to 50% in terms of accuracy.     

Two collaborative filtering recommender systems based on sparse dictionary coding

This paper proposes two types of recommender systems based on sparse dictionary coding. Firstly, a novel predictive recommender system that attempts to predict a user’s future rating of a specific item. Secondly, a top-n recommender system which finds a list of items predicted to be most relevant for a given user. The proposed methods are assessed using a variety of different metrics and are shown to be competitive with existing collaborative filtering recommender systems. Specifically, the sparse dictionary-based predictive recommender has advantages over existing methods in terms of a lower computational cost and not requiring parameter tuning. The sparse dictionary-based top-n recommender system has advantages over existing methods in terms of the accuracy of the predictions it makes and not requiring parameter tuning. An open-source software implemented and used for the evaluation in this paper is also provided for reproducibility.     

Detecting Communities in <Emphasis Type="Italic">K</Emphasis>-Partite <Emphasis Type="Italic">K</Emphasis>-Uniform (Hyper)Networks

In social tagging systems such as Delicious and Flickr,users collaboratively manage tags to annotate resources.Naturally,a social tagging system can be modeled as a (user,tag,resource) hypernetwork,where there are three different types of nodes,namely users,resources and tags,and each hyperedge has three end nodes,connecting a user,a resource and a tag that the user employs to annotate the resource.Then how can we automatically cluster related users,resources and tags,respectively? This is a problem of community detection in a 3-partite,3-uniform hypernetwork.More generally,given a K-partite K-uniform (hyper)network,where each (hyper)edge is a K-tuple composed of nodes of K different types,how can we automatically detect communities for nodes of different types? In this paper,by turning this problem into a problem of finding an efficient compression of the (hyper)network’s structure,we propose a quality function for measuring the goodness of partitions of a K-partite K-uniform (hyper)network into communities,and develop a fast community detection method based on optimization.Our method overcomes the limitations of state of the art techniques and has several desired properties such as comprehensive,parameter-free,and scalable.We compare our method with existing methods in both synthetic and real-world datasets.     

Alleviating the new user problem in collaborative filtering by exploiting personality information

The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3–40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.     

A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation

Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. Traditional collaborative approaches compute a similarity value between the current user and each other user by taking into account their rating style, that is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, collaborative algorithms compute recommendations for the current user. The problem with this approach is that the similarity value is only computable if users have common rated items. The main contribution of this work is a possible solution to overcome this limitation. We propose a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles. In more detail, user profiles are clustered to discover current user neighbors. Content-based user profiles play a key role in the proposed hybrid recommender. Traditional keyword-based approaches to user profiling are unable to capture the semantics of user interests. A distinctive feature of our work is the integration of linguistic knowledge in the process of learning semantic user profiles representing user interests in a more effective way, compared to classical keyword-based profiles, due to a sense-based indexing. Semantic profiles are obtained by integrating machine learning algorithms for text categorization, namely a naïve Bayes approach and a relevance feedback method, with a word sense disambiguation strategy based exclusively on the lexical knowledge stored in the WordNet lexical database. Experiments carried out on a content-based extension of the EachMovie dataset show an improvement of the accuracy of sense-based profiles with respect to keyword-based ones, when coping with the task of classifying movies as interesting (or not) for the current user. An experimental session has been also performed in order to evaluate the proposed hybrid recommender system. The results highlight the improvement in the predictive accuracy of collaborative recommendations obtained by selecting like-minded users according to user profiles.     

Coherence and inconsistencies in rating behavior: estimating the magic barrier of recommender systems

Recommender Systems have to deal with a wide variety of users and user types that express their preferences in different ways. This difference in user behavior can have a profound impact on the performance of the recommender system. Users receive better (or worse) recommendations depending on the quantity and the quality of the information the system knows about them. Specifically, the inconsistencies in users’ preferences impose a lower bound on the error the system may achieve when predicting ratings for one particular user—this is referred to as the magic barrier. In this work, we present a mathematical characterization of the magic barrier based on the assumption that user ratings are afflicted with inconsistencies—noise. Furthermore, we propose a measure of the consistency of user ratings (rating coherence) that predicts the performance of recommendation methods. More specifically, we show that user coherence is correlated with the magic barrier; we exploit this correlation to discriminate between easy users (those with a lower magic barrier) and difficult ones (those with a higher magic barrier). We report experiments where the recommendation error for the more coherent users is lower than that of the less coherent ones. We further validate these results by using two public datasets, where the necessary data to identify the magic barrier is not available, in which we obtain similar performance improvements.     

Interactive transmission processing for large images in a resource-constraint mobile wireless network

In the state-of-the-art methods for (large) image transmission, no user interaction behaviors (e.g., user tapping) can be actively involved to affect the transmission performance (e.g., higher image transmission efficiency with relatively poor image quality). So, to effectively and efficiently reduce the large image transmission costs in resource-constraint mobile wireless networks (MWN), we design a content-based and bandwidth-aware Interactive large Image Transmission method in MWN, called the I it. To the best of our knowledge, this is the first study on the interactive image transmission. The whole transmission processing of the I it works as follows: before transmission, a preprocessing step computes the optimal and initial image block (IB) replicas based on the image content and the current network bandwidth at the sender node. During transmission, in case of unsatisfied transmission efficiency, the user’s anxiety to preview the image can be implicitly indicated by the frequency of tapping the screen. In response, the transmission resolutions of the candidate IB replicas can be dynamically adjusted based on the user anxiety degree (UAD). Finally, the candidate IB replicas are transmitted with different priorities to the receiver for reconstruction and display. The experimental results show that the performance of our approach is both efficient and effective, minimizing the response time by decreasing the network transmission cost while improving user experiences.     

Discerning individual interests and shared interests for social user profiling

Traditionally, research about social user profiling assumes that users share some similar interests with their followees. However, it lacks the studies on what topic and to what extent their interests are similar. Our study in online sharing sites reveals that besides shared interests between followers and followees, users do maintain some individual interests which differ from their followees. Thus, for better social user profiling we need to discern individual interests (capturing the uniqueness of users) and shared interests (capturing the commonality of neighboring users) of the users in the connected world. To achieve this, we extend the matrix factorization model by incorporating both individual and shared interests, and also learn the multi-faceted similarities unsupervisedly. The proposed method can be applied to many applications, such as rating prediction, item level social influence maximization and so on. Experimental results on real-world datasets show that our work can be applied to improve the performance of social rating. Also, it can reveal some interesting findings, such as who likes the “controversial” items most, and who is the most influential in attracting their followers to rate an item.     

Almost cover-free codes

We say that an s-subset of codewords of a code X is (s, l)-bad if X contains l other codewords such that the conjunction of these l words is covered by the disjunction of the words of the s-subset. Otherwise, an s-subset of codewords of X is said to be (s, l)-bad. A binary code X is called a disjunctive (s, l) cover-free (CF) code if X does not contain (s, l)-bad subsets. We consider a probabilistic generalization of (s, l) CF codes: we say that a binary code is an (s, l) almost cover-free (ACF) code if almost all s-subsets of its codewords are (s, l)-good. The most interesting result is the proof of a lower and an upper bound for the capacity of (s, l) ACF codes; the ratio of these bounds tends as s→∞ to the limit value log₂ e/(le).     

Social influence determination on big data streams in an online social network

Social networks have become a good place to promote products and also to campaign for causes. Maximizing the spread of information in an online social network at a least cost has attracted the attention of publicist’s. In general, influence user ranking methods are derived either by a network’s topological features or by user features but not both. Existing Influence Maximization Problem (IMP) operates as a modification of greedy algorithms that cannot scale streaming data. Which are time consuming and cannot handle large networks because it requires heavy Monte-Carlo simulation. This is also an NP hard problem in both linear threshold and independent cascade models. Our proposed work aims to address IMP through a Rank-based sampling approach in the Map-Reduce environment. This novel technique combines user and topological features of the network enabling it to handle real-time streaming data. Our experiment of influenced rank-based sampling approach to influence maximization is compared to the greedy approach with and without sampling that exhibits an accuracy of 82%. Performance analysis in terms of running time is reduced from O(n ³) to O(k n). Where ‘k’ is the size of the sample dataset and ‘n’ is the number of user’s.     

Integrating tourist packages and tourist attractions for personalized trip planning based on travel constraints

In recent years, with the upgrading of mobile positioning and the popularity of smart devices, location related research gets a lot of attentions. One of popular issues is the trip planning problem. Although many related scientific or technical literature have been proposed, most of them focused only on tourist attraction recommendation or arrangement meeting some user demands. In fact, to grasp the huge tourism opportunities, more and more tour operators design tourist packages and provide to users. Generally, tourist packages have many advantages such as cheaper ticket price and higher transportation convenience. However, researches on trip planning combining tourist packages have not been mentioned in the past studies. In this research, we present a new approach named Package-Attraction-based Trip Planner (PAT-Planner) to simultaneously combine tourist packages and tourist attractions for personalized trip planning satisfying users’ travel constraints. In PAT-Planner, we first based on user preferences and temporal characteristics to design a Score Inference Model for respectively measuring the score of a tourist package or tourist attraction. Then, we develop the Hybrid Trip-Mine algorithm meeting user travel constraints for personalized trip planning. Besides, we further propose two improvement strategies, namely Score Estimation and Score Bound Tightening, based on Hybrid Trip-Mine to speed up the trip planning efficiency. As far as we know, our study is the first attempt to simultaneously combine tourist packages and tourist attractions on trip planning problem. Through a series of experimental evaluations and case studies using the collected Gowalla datasets, PAT-Planner demonstrates excellent planning effects.     

Integrated microblog sentiment analysis from users’ social interaction patterns and textual opinions

Traditional post-level opinion classification methods usually fail to capture a person’s overall sentiment orientation toward a topic from his/her microblog posts published for a variety of themes related to that topic. One reason for this is that the sentiments connoted in the textual expressions of microblog posts are often obscure. Moreover, a person’s opinions are often influenced by his/her social network. This study therefore proposes a new method based on integrated information of microblog users’ social interactions and textual opinions to infer the sentiment orientation of a user or the whole group regarding a hot topic. A Social Opinion Graph (SOG) is first constructed as the data model for sentiment analysis of a group of microblog users who share opinions on a topic. This represents their social interactions and opinions. The training phase then uses the SOGs of training sets to construct Sentiment Guiding Matrix (SGM), representing the knowledge about the correlation between users’ sentiments, Textual Sentiment Classifier (TSC), and emotion homophily coefficients of the influence of various types of social interaction on users’ mutual sentiments. All of these support a high-performance social sentiment analysis procedure based on the relaxation labeling scheme. The experimental results show that the proposed method has better sentiment classification accuracy than the textual classification and other integrated classification methods. In addition, IMSA can reduce pre-annotation overheads and the influence from sampling deviation.     

An effective suggestion method for keyword search of databases

This paper solves the problem of providing high-quality suggestions for user keyword queries over databases. With the assumption that the returned suggestions are independent, existing query suggestion methods over databases score candidate suggestions individually and return the top-k best of them. However, the top-k suggestions have high redundancy with respect to the topics. To provide informative suggestions, the returned k suggestions are expected to be diverse, i.e., maximizing the relevance to the user query and the diversity with respect to topics that the user might be interested in simultaneously. In this paper, an objective function considering both factors is defined for evaluating a suggestion set. We show that maximizing the objective function is a submodular function maximization problem subject to n matroid constraints, which is an NP-hard problem. An greedy approximate algorithm with an approximation ratio O(\(\frac {1}{1+n}\)) is also proposed. Experimental results show that our suggestion outperforms other methods on providing relevant and diverse suggestions.     

Mining coterie patterns from Instagram photo trajectories for recommending popular travel routes

Instagram is a popular photo-sharing social application. It is widely used by tourists to record their journey information such as location, time and interest. Consequently, a huge volume of geo-tagged photos with spatio-temporal information are generated along tourist’s travel trajectories. Such Instagram photo trajectories consist of travel paths, travel density distributions, and traveller behaviors, preferences, and mobility patterns. Mining Instagram photo trajectories is thus very useful for many mobile and location-based social applications, including tour guide and recommender systems. However, we have not found any work that extracts interesting group-like travel trajectories from Instagram photos asynchronously taken by different tourists. Motivated by this, we propose a novel concept: coterie, which reveals representative travel trajectory patterns hidden in Instagram photos taken by users at shared locations and paths. Our work includes the discovery of (1) coteries, (2) closed coteries, and (3) the recommendation of popular travel routes based on closed coteries. For this, we first build a statistically reliable trajectory database from Instagram geo-tagged photos. These trajectories are then clustered by the DBSCAN method to find tourist density. Next, we transform each raw spatio-temporal trajectory into a sequence of clusters. All discriminative closed coteries are further identified by a Cluster-Growth algorithm. Finally, distance-aware and conformityaware recommendation strategies are applied on closed coteries to recommend popular tour routes. Visualized demos and extensive experimental results demonstrate the effectiveness and efficiency of our methods.     

Reverse direction-based surrounder queries for mobile recommendations

This paper proposes a new spatial query called a reverse direction-based surrounder (RDBS) query, which retrieves a user who is seeing a point of interest (POI) as one of their direction-based surrounders (DBSs). According to a user, one POI can be dominated by a second POI if the POIs are directionally close and the first POI is farther from the user than the second is. Two POIs are directionally close if their included angle with respect to the user is smaller than an angular threshold ??. If a POI cannot be dominated by another POI, it is a DBS of the user. We also propose an extended query called competitor RDBS query. POIs that share the same RDBSs with another POI are defined as competitors of that POI. We design algorithms to answer the RDBS queries and competitor queries. The experimental results show that the proposed algorithms can answer the queries efficiently.     

Hybrid full-/half-duplex cellular networks: user admission and power control

We consider a single-cell network with a hybrid full-/half-duplex base station. For the practical scenario with N channels, K uplink users, and M downlink users (max{K,M} ≤ N ≤ K + M), we tackle the issue of user admission and power control to simultaneously maximize the user admission number and minimize the total transmit power when guaranteeing the quality-of-service requirement of individual users. We formulate a 0–1 integer programming problem for the joint-user admission and power allocation problem. Because finding the optimal solution of this problem is NP-hard in general, a low-complexity algorithm is proposed by introducing the novel concept of adding dummy users. Simulation results show that the proposed algorithm achieves performance similar to that of branch and bound algorithm and significantly outperforms the random pairing algorithm.     

Optimisation of a non linear fuzzy function

 We present a first study concerning the optimization of a non linear fuzzy function f depending both on a crisp variable and a fuzzy number: therefore the function value is a fuzzy number. More specifically, given a real fuzzy number ?∈F and the function f(a,x):R ²→R, we consider the fuzzy extension induced by f, f˜ : F × R → F, f˜(?,x) = Y˜. If K is a convex subset of R, the problem we consider is “maximizing”f˜(?,x), xˉ∈ K. The first problem is the meaning of the word “maximizing”: in fact it is well-known that ranking fuzzy numbers is a complex matter. Following a general method, we introduce a real function (evaluation function) on real fuzzy numbers, in order to get a crisp rating, induced by the order of the real line. In such a way, the optimization problem on fuzzy numbers can be written in terms of an optimization problem for the real-valued function obtained by composition of f with a suitable evaluation function. This approach allows us to state a necessary and sufficient condition in order that ∈K is the maximum for f˜ in K, when f(a,x) is convex-concave (Theorem 4.1).     

On transitive partitions of an <Emphasis Type="Italic">n</Emphasis>-cube into codes

We present methods to construct transitive partitions of the set E ⁿ of all binary vectors of length n into codes. In particular, we show that for all n = 2 ^k ? 1, k ≥ 3, there exist transitive partitions of E ⁿ into perfect transitive codes of length n.     

Flexible integration of multimedia sub-queries with qualitative preferences

Complex multimedia queries, aiming to retrieve from large databases those objects that best match the query specification, are usually processed by splitting them into a set of m simpler sub-queries, each dealing with only some of the query features. To determine which are the overall best-matching objects, a rule is then needed to integrate the results of such sub-queries, i.e., how to globally rank the m-dimensional vectors of matching degrees, or partial scores, that objects obtain on the m sub-queries. It is a fact that state-of-the-art approaches all adopt as integration rule a scoring function, such as weighted average, that aggregates the m partial scores into an overall (numerical) similarity score, so that objects can be linearly ordered and only the highest scored ones returned to the user. This choice however forces the system to compromise between the different sub-queries and can easily lead to miss relevant results. In this paper we explore the potentialities of a more general approach, based on the use of qualitative preferences, able to define arbitrary partial (rather than only linear) orders on database objects, so that a larger flexibility is gained in shaping what the user is looking for. For the purpose of efficient evaluation, we propose two integration algorithms able to work with any (monotone) partial order (thus also with scoring functions): MPO, which delivers objects one layer of the partial order at a time, and iMPO, which can incrementally return one object at a time, thus also suitable for processing top k queries. Our analysis demonstrates that using qualitative preferences pays off. In particular, using Skyline and Region-prioritized Skyline preferences for queries on a real image database, we show that the results we get have a precision comparable to that obtainable using scoring functions, yet they are obtained much faster, saving up to about 70% database accesses.