Social-Textual Search and Ranking∗ Ali Khodaei Cyrus Shahabi Department of Computer Science Department of Computer Science University of Southern California University of Southern California Los Angeles,CA 90089,USA Los Angeles,CA 90089,USA khodaei@usc.edu shahabi@usc.edu ABSTRACT ditional web where users are often in read-only mode, Web Web search engines are traditionally focused on textual con- 2.0 have enabled users to be in read-write mode. In other tent of data. Emergence of social networks and Web 2.0 words, users have started to express themselves in the forms applications makes it interesting to see how social data can of generating and publishing content (e.g., writing a tweet), be used in improving the conventional textual search on the re-sharing interesting content by others (e.g., re-tweeting) web. In this paper, we focus on how to improve the ef- and rating/evaluating the existing content (e.g., choosing a fectiveness of web search by utilizing social data available favorite tweet). This emergence of social networks and Web from users, users actions and their underlying social net- 2.0 resulted in huge amount of data available that can be work on the web. We define and formalize the problem of utilized in many domains. social-textual (socio-textual ) search and show how social as- In this paper, we focus on taking advantage of this infor- pect of the web can be effectively integrated into the textual mation in the domain of (textual) web search. We argue that search engines. We propose a new social relevance ranking by integrating information from users’ social networks and based on several parameters including relationship between their activities on the web, we can improve the conventional users, importance of each user and actions users perform on textual search and ranking. In today’s web, we can know the web documents (objects). We show how the proposed so- existence and degree of relationships among people and also cial ranking can be combined with the conventional textual at the same time have the knowledge of people’s interests de- relevance ranking. We have conducted an extensive set of rived from their actions/activities on the web. It is both in- experiments on the data from online radio website last.fm to tuitive and proven [3] that people have very similar interests evaluate the effectiveness of our proposed approaches. Our with their friends. Also, people tend to trust the opinions experimental results are very promising and show a signifi- and judgements of their friends more than strangers. We cant improvement for socio-textual ranking over textual only show how to modify the existing (textual) relevance rankings and social only approaches. to take into consideration user’s social network in generating ranked results to the search queries. Consider the following example. A user searches for ”funny video clip”. Using con- 1. INTRODUCTION ventional textual search, user will receive a ranked results Social networks on the web have grown significantly over of some funny video clips. On the other hand, using user’s the past few years. People have started to reconstruct their social network, videos contain query keywords (i.e., funny friendship networks in the virtual world and many of these video clips) that have more comments, likes or favorites by virtual relationships are good representatives of their actual user’s friends should be ranked higher. However, the new (friendship) networks in the real world. At the same time ranked ranking is not trivial. Do we give more weights to and with the emergence of Web 2.0, many web users have textual keywords or to the social network? With social as- started to engage more with the web. In contrast to the tra- pect of the ranking, do we need to assign different weights to different friends of the user? How about the popularity ∗This research is supported in part by the NSF grant of the users (friends) in general? Also, what are the actions IS-1115153, the USC Integrated Media Systems Center that are important for objects and how we quantify those? (IMSC), and also by unrestricted cash and equipment gifts In order to combine social data into textual relevance rank- from Google, Microsoft and Qualcomm. The opinions, find- ings, and conclusions or recommendations expressed in this ing, social relevance between users and objects (documents) publication are those of the authors and do not necessarily has to be defined first. In order to model social relevance, reflect the views of the National Science Foundation. existence and degree of relationships between users have to be taken into consideration. Also, actions permitted for each type of document (object) and their importance should be modeled. Finally, overall importance/impact of each user has to be considered as well. We first review the few existing studies regarding social Copyright c 2012 for the individual papers by the papers’ authors. Copy- search and utilization of social networks in the web search. ing permitted for private and academic purposes. This volume is published Then, we define and formalize our problem. Next, we present and copyrighted by its editors. new scoring methods to calculate social relevance between CrowdSearch 2012 workshop at WWW 2012, Lyon, France users and documents (objects). We show how the impor- tance of users, different relationships among users and ac- or +1ed that result. Their algorithms are not public and it tions they perform on objects can impact the final relevance seems that they only show the likes and +1s and the actual ranking. After proposing the new social relevance model, we ranking is not affected. present a novel socio-textual relevance ranking technique to There exists a relevant but somehow different topic of folk- combine textual and social relevance rankings. Finally, in sonomies. Tags and other conceptual structures in social our experimental section, we evaluate the effectiveness of tagging networks are called folksonomies. A folksonomy is our proposed models and show that our new relevance rank- usually interpreted as a set of user-tag-resource triplets. Ex- ing methods are effective and improve the accuracy of the isting work for social search on folksonomies is mainly on returned results. improving search process over social data (tags and users) gathered from social tagging sites [10][11][12]. In this con- 2. RELATED WORK text, relationships among users and tags and also among tags themselves are of significant importance. There are two groups of related work on the application Finally, there are few studies on the role of collaborative of social networks in search. With the first group, people filtering in this new social context. Role of social networks through their social networks are identified and contacted on collaborative filtering is studied in [3] and [13]. It is shown directly to answer web queries. In other words, queries are that using social networks in collaborative filtering and rec- directly sent to individuals and answer of the queries are ommendations makes the recommendations better in com- coming from people themselves [1, 16, 17]. In this approach parison with the traditional collaborative approaches. In an- called search services, people and their networks are indexed other direction, [14] studies the application of collaborative and a search engine has to find the most relevant people to filtering on a movie search engine. Authors propose to calcu- send the queries/questions to. late documents (movies) authorities based on users’ ratings The main focus of the second group is on the search pro- (using collaborative filtering) instead of pagerank and other cess over social data (tags, users and objects) from sites/application link-based authority measures. Social networks of users are with social aspect such as social tagging sites and (some) non-existent in this study. Web 2.0 applications. In [2], authors investigate a personal- In contrast to the above, our notion of social search is ized social search engine based on users’ relations. They to utilize exiting social networks to improve the accuracy study the effectiveness of three types of social networks: and relevance of convention textual web search. For us, familiarity-based, similarity-based and both. In [4], which search still has its core textual dimension, represented by is a short paper, authors propose two search strategies for textual keywords/content in the query and the documents. performing search on the web: textual relevance (TR)-based In parallel to the textual dimension, (querying) user’s social search and social influence (SI)-based search. In the former, network is exploited to make the final search results more the search is first performed according to the classical tf-idf relevant. Our focus is mostly on finding/modeling effective approach and then for each retrieved document the social in- measures to calculate the social relevance/ranking and com- fluence between its publisher and querying user is computed. bine it with the existing standard textual relevance rankings. The final ranking is based on both scores. In the latter, first We also take into consideration the actions users perform on the social influence of the users to the querying user is cal- documents (as described in Section 3). culated and users with high scores are selected. Then, for each document, the final ranking score is determined based on both TR and SI. In both strategies, two separate costly 3. DEFINITIONS AND FORMALIZATIONS steps are needed. Also, it is not clear how accurate are the In this section, we formally define and formalize the prob- ranking functions since there is no experimental evaluation lem of socio-textual search. for the effectiveness of the rankings. Objects: We assume a collection O = {o1 ,o2 ,...on } of n In a set of similar papers [5, 6, 7], authors propose sev- objects (documents). An object can be a traditional web eral social network-based search ranking frameworks. The document such as a news page or a business home page proposed frameworks consider both document contents and or a Web 2.0 object such as a YouTube video, a tweet, a the similarity between a searcher and document owners in Facebook status or any other similar entity. An object o is a social network. They also propose a new user similar- composed of a set of textual keywords Ko and a set of users ity algorithm (MAS) to calculate user similarity in a social Uo associated with it. Uo is a set of users with some type of network. In this set of papers, the focus is more on user actions on the object o (see actions below). similarity functions and how to improve those algorithms. Users and Social Network: There is a set U = {u1 ,u2 ,...um } Most of their experiments are limited to a small number of m users using the system. We also assume a social net- of queries on YouTube videos with 3 users, 15 queries and work modeled as a directed graph G = (V, E) whose nodes small number of textual keywords. Relevant (interesting) represent the users of the system and edges represent the result is a result (video) whose category is simialr/equal to ties (relationship) among the users. The most common type the dominant category of videos that searcher has uploaded. of relationship is the friendship relationship but other type In a relatively older paper [8], authors explore the possi- of relationships can be also applied (e.g. follow relationship bility of using online social networks to improve the search in Twitter). on the Internet. Although this paper is not very technical Actions: There is a set A = {a1 ,a2 ,...al } of l actions that , it provides some interesting intuitions on integration of users can perform on the objects. These actions represent social networks and web search. With regards to commer- the relationship between users and objects. For instance, in cial search engines, Bing and recently Google have started Twitter, users can perform the following actions on objects to integrate Facebook and Google+, respectively, to their (tweets): publish a tweet, retweet a tweet or make a tweet search process. For some search results, they show query as their favorite tweet. issuer’s friends (from his/her social network) that have liked Socio-Textual Query: A socio-textual query is defined as Q = hKq , Sq i, where Kq is the textual part of query speci- In this section, we propose a new social relevance model fied as a set of keywords in the query and Sq is the social part to calculate the social relevance between users and objects. of query specified as the user uq issuing the query and the We also show how to combine the proposed social relevance social network G. Since our social network is always G, it is model with an existing textual relevance model and intro- sufficient to define the socio-textual query as hKq , uq i. Note duce our socio-textual relevance ranking. that while the textual part of the query is always explicit in We first propose a new scoring approach to calculate the the query, the social part is often implicit. In other words, social relevance between an object o and a query q (issued by we can safely assume that the system (search engine) knows user qu ). Our social relevance ranking creates a new scoring the user issuing the query and also the underlying social net- framework to retrieve and rank objects based on the so- work, hence the social part of the query can be automatically cial dimension of the query and objects. In order to have an added to the textual part by the search engine.1 accurate scoring function and retrieve the most socially rele- User relevance: User ui is relevant to user uj if the net- vant results to the user, we consider three important factors: work distance from the node corresponding to ui to the node (1) relevance of each user to the query’s user, (2) importance corresponding to uj is less than or equal to a system defined of each user in general, and (3) relationship between users threshold value δ. The less the distance between two nodes, and actions they perform on each object. In the following the more (user) relevant are those two nodes (users) 2 . Net- we discuss each measure. work distance can be any of the existing network distances User Relatedness. We measure the relatedness of a in the literature. Two users with the network distance more user to the querying user (and hence to the query itself) than δ are considered non-relevant to each other. by the user relatedness function urf (uq , ui ). There are sev- Social relevance: Social relevance between the object o eral measures to calculate the relatedness/closeness of two and the query q is defined based on the social relationship nodes in a graph/social network. Some of the approaches that exists between the querying user (uq ) and users asso- consider the distance between nodes, some look at the be- ciated with the object o (Uo ). Object o and query q are haviors of users in a social network and some take into con- socially relevant if at least one of the object’s users (Uo ) is sideration number of mutual neighbors of two nodes. While user relevant with the user issuing the query. The larger the the required data is available, any of the above methods or user relevance is, the more socially relevant o and q are. We other exiting methods can be used for the user relatedness denote social relevance of object o to query q by socRel(o, q). function as long as the following three constraints are sat- We define social relevance in more details in Section 4. isfied: (1) urf (ui , ui ) = 1, (2) 0 ≤ urf (ui , uj ) ≤ 1 and Textual relevance: Object o is textually relevant to the the more relevant the users, the higher the value, and (3) query q if there exists at least one keyword belonging to both urf (ui , uj ) = 0 when urf (ui , uj ) < δ. The first constraint o and q, i.e., Kq ∩ Ko 6= ∅. We represent textual relevance states that each user is the most related user to herself. The of object o to query q by texRel(o, q). 3 second constraint normalizes this measure and also ensures Socio-textual relevance: Object o is social-textual (socio- that the more related users are assigned higher scores. Fi- textual) relevant to the query q if it is both socially and tex- nally, third constraint filters out all relationships that their tually relevant to the query q. Socio-textual relevance can significance is below a certain threshold (δ). As a simple be defined by a monotonic scoring function F of textual and example satisfying all the above constraints and also cap- social relevances. For example, F can be the weighted sum turing the relatedness among users, we can use an inverse of of the social and textual relevances: distance between users (nodes) in the social network (graph) as follows: urf (ui , uj ) = dist(u1i ,uj ) F (o, q) = α.socRel(o, q) + (1 − α).texRel(o, q) (1) where dist(ui , uj ) is the number of edges in a shortest . α is a parameter assigning relative weights to social and path connecting ui and uj . textual relevances. The output of function F (o, q) is the User Weight. We quantify the overall (global) impor- socio-textual relevance score of the object o for the query q, tance of each user by the user weight function uwf (ui ). This and is denoted by stRel(o, q). In Section 4 we show how to measure quantifies the significance of a user in its social net- calculate socio-textual relevance. work. For instance, for Twitter, a user with many followers Socio-textual search: A socio-textual search identifies should be assigned a higher weight than a user with only and ranks all the objects that are socio-textual relevant to few followers, or for Facebook, a user with more friends is the query q. The result is the top-k objects sorted based on more important to the social network than a user with fewer objects’ socio-textual relevance scores. The parameter k is friends. In the field of graph theory and social networks this determined by the user. value is called centrality and there exist several approaches to measure it. Four popular methods to compute centrality 4. SOCIAL RELEVANCE RANKING are: degree centrality, betweenness, closeness, and eigenvec- tor centrality. [9] is a good resource For a review of these 1 Naturally, here and in other parts of this paper, we consider methods and further reading. Similar to the user relatedness only users who willingly make their social information public function, the user weight function is also general enough and to the system. most of the existing approaches can be applied to uwf . As 2 For simplicity of presentation, from now on, we assume that an example for this function we can use the degree central- users’ social network is implemented as an undirected graph. ity of nodes (users) as an indication of their importance as Hence, user relevance and other relationships between users follows: will be symmetric. 3 uwf (ui ) = deg(u m−1 i) In this paper, we do not focus on textual relevance mod- els. We use popular tf-idf model when we need to calculate where deg(ui ) is the number of edges incident upon ui and textual relevance. m is number of nodes (users). User Action. The importance of each user for each ob- query keywords (tf), and 2) more important query keywords ject is directly related to the action(s)4 user perform on (idf), in our social relevance model, more weight is given to each object. Publishing/owning an object by a user shows the objects with 1) more important actions 2) performed by a higher weight/relevance between the object and the user more important users 3) whom are more related (closer) to than only commenting on the object. For instance, a user the querying user. uploading (and thus owning) a YouTube video is more sig- nificant to that video than a user who only comments on 4.1 Socio-Textual Search that video. The importance/relevance of each user to an In this section, we combine social relevance with an exist- object is measured by the user action function uaf (ui , ok ) ing textual relevance model (tf-idf) to calculate the overall and is dependant on the type of action user ui performs on socio-textual relevance of the object o with query q with re- object ok . For each system, weight/significance of each ac- gards to both social and textual dimensions. Socio-textual tion should be determined based on specific characteristics relevance ranking considers both the textual relevance of the of that system. We normalize the value of uaf by assign- objects to the query and also the social relevance of the ob- ing values between 0 and 1 (inclusive) to it. The higher jects to the query. We formulate the socio-textual relevance the value is, the more important/relevant is the user to the ranking as follows: object. With some systems, there exist actions that can be performed multiple times by a user on an object, and the more the action is performed the higher is the relevance stRel(o, q) = α × socRel(o, q) + (1 − α) × texRel(o, q) between the user and that object. For instance, in an on- X = α× urf (uq , vi ) × uaf (vi , o) × uwf (vi ) line radio website (e.g. last.fm5 ), action listening to a track vi ∈Uo can be done multiple times by a user. The more the user X chooses to listen to a track, the more relevant/significant is + (1 − α) × tf (o, tj ) × idf (tj ) that track to the user. Below, we show examples of differ- tj ∈Kq ent actions and their corresponding weights for four popular (3) web 2.0. objects. Note that the assigned weights are only our suggestion, they can (and should) be easily changed for where stRel(o, q) is the socio-textual relevance of object different applications and/or settings. Examples are as fol- o to query q where user uq is the query issuer; socRel and lows: texRel are corresponding social and textual relevances for object o; urf , uaf and uwf are user-related functions as • YouTube videos: Actions = {own(publish) : 1, f avorite : described above; Kq is set of query keywords (tags) and tj s 0.9, like : 0.7, comment : 0.4}. are individual query keywords (tags); tf (o, tj ) is term fre- quency function determining relevance of term tj to object • Twitter tweets: Actions = {tweet(publish) : 1, f avorite : o; idf (tj ) is inverted document frequency function determin- 0.9, retweet : 0.5}. ing the importance of keyword tj in the entire collection; and α is a parameter giving relative weights to social and • Facebook objects: Actions = {own(publish) : 1, like : textual importance. Not only the implementation of urf , 0.8, share : 0.6, comment : 0.4}. uaf and uwf functions are flexible (see Section 4), also the • last.fm tracks (songs): Actions = {like : 0.8, tag : implementation of texRel is flexible. Although, we used playcount 0.5, comment : 0.4, listen : max }. the conventional tf-idf model for capturing the textual rele- playcount vancy, any other textual relevance (similarity) function can Note that for last.fm, we see an example of an action (listen) be also used. Equation 3 provides the general framework that can be performed multiple times (keep in mind that for calculating socio-textual relevance and implementation many other actions in our examples also can be performed and/or importance of each weight can be changed based on multiple times). The above model can be applied to other the context and users/applications needs. object types for different web, web 2.0 and non-web objects. It is simple, flexible and easy to update/change based on 5. EXPERIMENTAL EVALUATION different applications and purposes. It shows and quantifies In this section, we evaluate the effectiveness of our pro- what users are important/relevant for each object and how posed approaches. First, we describe the dataset, the set- much is this relevance/importance for each user/object. tings and the queries used for the experiments. Next, we Now, we propose the final scoring function to calculate show and discuss the results. the social relevance between object o and query q as follows: Data. There are very few publicly available datasets for experimentation that include both friendships (social net- X socRel(o, q) = urf (uq , vi ) × uaf (vi , o) × uwf (vi ) (2) work) and textual keywords (tags). One very good dataset vi ∈Uo is a dataset generated by [3] from a Web 2.0 website last.fm. Since this dataset has both social and textual components In Equation 2, uq is the user issuing the query and Uo is needed for our setting, we used this dataset. Last.fm is a the set of users with some actions on the object o. While in music social network that allows users to listen to different classical textual relevance models such as tf-idf, more weight music tracks, tag them with textual keywords and at the is given to the objects (documents) with 1) more number of same time make friendships with other people on the net- 4 for simplicity, we assume that each user can perform at work. While the users listen to a track they have the ability most one action on each object. However, our model can to either move to the next track of the playlist or keep lis- easily be generalized for multiple actions per user. tening to the same. These actions can be interpreted as 5 http://www.last.fm/ explicit negative and implicit positive feedback respectively 0.8 0.8 0.8 0.7 0.7 0.7 0.6 0.6 0.6 0.5 soc text 0.5 soc text 0.5 soc text sotext socBinary sotext socBinary sotext socBinary sotextBinary sotextBinary sotextBinary 0.4 0.4 0.4 1 2 5 10 20 1 2 5 10 20 1 2 5 10 20 (a) Setting 1 (b) Setting 2 (c) Setting 3 Figure 1: Impact of k on nDCG 0.8 soc text 0.8 soc text 0.8 soc text sotext socBinary sotext socBinary sotext socBinary 0.7 sotextBinary 0.7 sotextBinary 0.7 sotextBinary 0.6 0.6 0.6 0.5 0.5 0.5 0.4 0.4 0.4 1 2 3 4 1 2 3 4 1 2 3 4 (a) Setting 1 (b) Setting 2 (c) Setting 3 Figure 2: Impact of δ on nDCG [3]. The dataset used contains 3148 users, 30520 tracks, proach computes the results based on our socio-textual rel- 12565 tags and 5616 unique bonds of friendship among the evance model discussed in Section 4.1. Finally, socBinary users collected, which was made freely available by [3]. In and sotextBinary are simplified versions of soc and sotext our context (search), each track is a document (object) con- approaches in which action listening only has the binary sisting of several textual keywords (tags). Users search for value 0 or 1 (instead of the actual playcount value). In desired documents (tracks) by specifying one or more tex- other words, the value of user action function is calculated tual keywords (tags). as follows: Actions. The only information available from last.fm uaf (ui , ok ) = 1 if playcount(ui , ok ) > 0, uaf (ui , ok ) = 0 website is the number of times a user listens to a track. This otherwise. value is called playcount and is a very important indicator of For each query, when using soc,sotext, socBinary and so- relevance/importance between the user and the track. This textBinary approaches, we do not use the existing informa- is an action that can be performed multiple times on a track tion regarding the relationship/actions between the querying (user listening to a track multiple times). We use this ac- user and the objects (tracks). This is done to be fair and tion to calculate the value of user action function as follows: be able to evaluate the social component of the approaches playcount(ui ,ok ) uaf (ui , ok ) = max−playcount(u where playcount(u i , o k ) is based only on user’s social network and friends. i) the number of times user ui listens to track ok without skip- Settings. We evaluate the results for three different set- ping the song and max − playcount(ui ) is the maximum tings. Setting1 is the default setting with all the details playcount value among all tracks that user ui has listened described so far. Setting2 is a subset of setting1 in which to. queries that generate fewer than k results are pruned (and Queries. For each query, we randomly chose 1 to 3 tex- not evaluated). Finally, setting3 is a subset of setting2 in tual keywords (tags) from the list of all the tags in our which queries with querying users with less than 8 immedi- dataset, and one random user from the list of all users with ate neighbors are pruned. the minimum of 4 friends in the system. We filtered out Evaluation Metric. We evaluated the accuracy of the queries that did not generate any results. Queries are per- methods under comparison using the most common stan- formed in rounds. Each round consists of 100 queries and is dard metric: nDCG (normalized discounted cumulative gain) conducted for each input setting. [15]. When computing nDCG, we consider the playcount Ground Truth. To evaluate our results, we have to com- value as the relevance value. IDCG (ideal DCG) is the re- pare them with a ranking that is the most relevant ranking sults generated by our ground truth. to the user (ground truth). Since playcount indicates the real interest of each user to each track, we leverage playcount to 5.1 Results construct the most relevant list (ranking) for each query as follows: for each query, we return list of all textually relevant 5.1.1 Varying k tracks (tracks contain one or more of the query keywords), With the first set of experiment, we evaluate the effec- order them based on the querying user’s playcount values tiveness of the proposed approaches by varying number of and return the top k results. requested results k. We report the average nDCG for each Approaches. We computed top-k query results for each round. Here, we fix the number of keywords at 1, alpha at query using the following five approaches: soc, text, sotext, 0.5 and the threshold value δ at 2. The value of k varies socBinary and sotextBinary. soc approach generates the re- from 1 to 20. The results for three settings are shown in sults based on the social relevance model only (presented Figures 1(a), 1(b) and 1(c), respectively. The first observa- in Section 4). text approach generates the results based tion is that sotext is the most effective approach among all on the conventional tf-idf relevance model only. sotext ap- the three settings. This is very promising since it shows that 0.8 0.8 0.8 0.7 0.7 0.7 0.6 0.6 0.6 0.5 soc text 0.5 soc text 0.5 soc text sotext socBinary sotext socBinary sotext socBinary sotextBinary sotextBinary sotextBinary 0.4 0.4 0.4 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 (a) Setting 1 (b) Setting 2 (c) Setting 3 Figure 3: Impact of alpha on nDCG combining the textual relevance and social relevance using documents based on both their social and textual features. our model generates more relevant/accurate results in com- We proposed a new scoring model to calculate social rele- parison with using only textual relevance or social relevance. vance between documents and users. The proposed social The second observation is that sotext and soc are superior relevance ranking utilizes the querying user’s social network to their corresponding binary approaches (sotextBinary and and actions her friends perform on web documents (objects) socBinary). This implies that using a more detailed action to generate more accurate results for her (textual) searches. model will improve the accuracy of the results. The third We also showed how to combine the new social relevance observation is that the results for our approaches are get- with the textual relevance model. We performed a set of ting better from setting1 to setting2 and from setting2 to experiments on the real dataset of last.fm and proved that setting3. This shows that 1) social-related approaches are the new approach is superior to the existing approaches. even better for more realistic settings, and 2) socially-related approaches generate more accurate results when users have 7. REFERENCES more neighbors (more socially connected). [1] D. Horowitz et al., The anatomy of a large-scale social search engine. WWW 2010. 5.1.2 Varying δ [2] D. Carmel et al., Personalized social search based on In the second set of our experiments, we evaluate the im- the users social network. CIKM 2009. pact of changing the threshold value δ. For different rounds, [3] I. Konstas et al., On social networks and collaborative we set the threshold value to 1,2,3 and 4. In this set of ex- recommendation. SIGIR 2009. periments, we fix the number of query keywords at 1, k at [4] P. Yin et al., On top-k social web search. CIKM 2010. 5 and alpha at 0.5. The results for three settings are shown [5] L. Gou et al., SNDocRank : Document Ranking Based in Figures 2(a), 2(b), and 2(c), respectively. Again, for all on Social Networks. WWW 2010. cases sotext easily outperforms the other four approaches. [6] L. Gou et al., SNDocRank: A social network-based As expected, the accuracy increases for socially-related ap- video search ranking framework. MIR 2010. proaches as the threshold value increases (and obviously no change for text approach). Again, these figures confirm the [7] L. Gou et al., Social network document ranking. JCDL observation that sotext and sotextBinary are superior than 2010. sotextBinary and socBinary approaches. [8] A. Mislove et al., Exploiting social networks for internet search. HotNets 2006. 5.1.3 Varying alpha [9] L.C. Freeman et al., Centrality in social networks: In the final set of our experiments, we evaluate the im- Conceptual clarification. Social Networks, 1(3), pact of changing the value of alpha (relative weight of so- 215-239, 1979. cial and textual relevances) on the effectiveness of the pro- [10] M. Rawashdeh et al., Folksonomy-boosted social posed approaches. We vary the value of alpha from 0 (so- media search and ranking. ICMR 2011. cial only) to 1 (textual only). In this set of experiments, [11] R. Schenkel et al., Efficient top-k querying over we fix the number of query keywords at 1, k at 5 and δ at social-tagging networks. SIGIR 2008. 2. The results for all three settings are shown in Figures [12] S.A. Yahia et al., Efficient network aware search in 3(a), 3(b), and 3(c), respectively. The obvious observation collaborative tagging sites. VLDB 2008. is that the results do not change for textual or social only ap- [13] F. Liu et al., Use of social network information to proaches. The more interesting observation is the behavior enhance collaborative filtering performance. Expert of the two socio-textual approaches. While both show their Syst. Appl. 37, 7, 2010. poorest results on the boundaries (only social or only tex- [14] S. Park et al., Applying collaborative filtering tual), they present their best accuracy in the middle of the techniques to movie search for better ranking and range (when both textual and social relevance are consid- browsing. KDD 2007. ered almost equally). We have to note that for most cases, [15] K. Jarvelin et al., Cumulated gain-based evaluation of the best accuracy is achieved when the social relevance has IR techniques. ACM Transactions on Information a little more weight. Again, this set of experiments confirm Systems 2002. the above observations regarding the superiority of sotext [16] T. Yan et al., CrowdSearch: exploiting crowds for and also the improved accuracy of setting2 and setiing3. accurate real-time image search on mobile phones. MobiSys 2010. 6. CONCLUSION [17] M.J. Franklin et al., CrowdDB: answering queries with In this paper, we introduced the problem of ranking web crowdsourcing. SIGMOD 2011.