D espite the deployment of a pleth- ora of practical systems, the theories
D espite the deployment of a pleth- ora of practical systems, the theories
and technologies for group recommen- dation aren’t very mature in compari- son to traditional RSs, which in turn will provide researchers many oppor- tunities and remain a large space to address in this area. It’s likely that fu- ture work will continue in two main streams. First, there’s the open issue of how to profile the group by con- sidering both preference agreements and disagreements. Next, with the in- creasing popularity of social networks, there’s still much to study about how to extract proper groups based on vari- ous criteria, and how to exploit group behaviors on social networks for group recommendations.
References
1. G. Xu, L. Li, and Y. Zhang, Web Mining and Social Networking, vol. 6, Springer, 2011.
2. S. Amer-Yahia et al., “Group Recom- mendation: Semantics and Efficiency,” Proc. VLDB Endowment, vol. 2, no. 1, 2009, pp. 754–765.
3. L. Cao, “In-Depth Behavior Understand- ing and Use: The Behavior Informatics Approach,” Information Sciences, vol. 180, no. 17, 2010, pp. 3067–3085.
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4. L. Cao, Y. Ou, and P.S. Yu, “Coupled Behavior Analysis with Applications,” IEEE Trans. Knowledge and Data Engineering, vol. 24, no. 8, 2012, pp. 1378–1392.
5. L. Baltrunas, T. Makcinskas, and F. Ricci, “Group Recommendations with Rank Aggregation and Collabora- tive Filtering,” Proc. 4th ACM Conf. Recommender Systems, 2010, pp. 119–126.
Guandong Xu is a senior lecturer in the Advanced Analytics Institute at University of Technology, Sydney. Contact him at guan- [email protected].
Zhiang Wu is an associate professor in the Jiangsu Provincial Key Laboratory of E-Busi- ness, Nanjing University of Finance and Eco- nomics. Contact him at [email protected].
Web Search and Behavior Gabriella Pasi, Università degli Studi di
Milano Bicocca Since a long time, information retrieval
has been considered a complex interac- tive task that engages a person using a search engine to proactively formulate
a query, and to interact with the system to localize in the considered repository those information objects that fulfill
her/his expectations. 1 Such a task im- plies learning and adapting to the user’s context, going well beyond the “blind” and closed behavior of the first-gener- ation search engines, centered on topi- cal relevance to a keyword-based query, and based on a “one-shot” input-output interaction that the user engages with a search engine by inputting a query. For anyone inputting the same query, the system produced the same results, irre- spective of the complexity of the needs and motivations behind that query— and independently from the user, the us- er’s context, and her/his evolving intents
and interests, which are well beyond the selection of a few keywords. Sub- sequent generations of search engines tried to overcome the limitations of first-generation search engines, on the basis of several analyses and findings. First, a deep study of the notion of rel- evance and its multidimensional nature determined the shift from topical rele- vance to multiple-relevance assessment; second, a deeper analysis of the interac- tive nature of the search task pointed out the dependence of search on sev- eral variables and situations, such as the task for which the search is undertaken, the characteristics and the cultural and personal background of users, and the awareness of properties and charac- teristics of the information objects and their nature. Research on information behavior has also put more emphasis on users and their context. 2
Search engines today go well be- yond topical relevance, and interac- tions of users with a search engine provide useful data for modeling the user’s search behavior. Several propos- als have appeared, pointing out the importance of adapting the system be- havior to the user’s world, which is in turn often captured and summarized through monitoring her/his interaction behavior with the search engine. Here, the two agents involved are a user and
a system, each acting and exhibiting their behaviors, which are often inde- pendently modeled from each other.
Personalized and Context-Aware Search The previously outlined research path evolved from systems fully unaware of the motivations and needs behind a query and only able to use a query as an object independent of the user (the so-called system-centered approach), to systems that have increasingly made use of various kinds of “background knowledge” around a query, including knowledge related to the user, to the
search domain, to the sought objects, and the query itself (the user-centered approach and beyond). This back- ground knowledge has been conceived as a crucial source of evidence of the needs behind a query, and has given rise to personal and contextual approaches to search, implying a shift from system- centered relevance to user- and context- centered relevance assessment.
As previously noted, interaction is the key issue in contextualizing a search activity, by tuning the system behavior to the user’s reality and ex- pectations. Being aware of the user’s and search’s context, the system may adapt to them, and in her/his turn the user may adapt her/his interactions with the system to tune its behavior.
In particular, search personaliza- tion has increasingly been studied as
a means to adapt the system behavior to the user and her/his context; to this aim the typical approach is to define
a user model, which synthesizes the user preferences and interests. 3 The difficult task of eliciting and represent- ing a user model has been tackled by two distinct approaches: by implic- itly monitoring the user system inter- actions, and by explicitly asking the user to provide useful information to describe her/his interest. The for- mer approach has been the more fol- lowed, due to its unobtrusiveness and easiness of implementation, especially for search engine companies that can easily analyze query logs and click through information. While interact- ing with a search engine, users for- mulate and reformulate queries, they click on proposed search results, spend variable time on examined pages, download them, and perform other
actions. 4 Search engines today make use of information taken from user in- teraction behavior to enhance search through various approaches. The wide variety of interactions with search en- gines is monitored to give systems a
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few hints on the information-seeking tasks of users. However, from an oper- ational point of view the relationships between users, their tasks, and infor- mation objects aren’t yet exploited enough. Context in fact should be de- termined based more on interactions and coupling relationships than by exploiting a limited snapshot of a few pieces of knowledge around users and information objects.
Social and Collaborative Behavior
A challenging and more recent research direction is related to exploiting the us- er’s social context to improve search. Considering the user’s behavior in social contexts might be important when the user undertakes a complex search activ- ity. This can be done either by making use of social interactions or by gathering data about the actions undertaken by
a large group of individuals (either on Web usage, or in social networks), with the goal of better understanding the us- er’s needs and context. A second way to use social information is related to the so-called collaborative search approach that may be considered a subset of so- cial search, where people work together to satisfy an information need. 5
A limitation of social approaches might be related to the fact that the in- formation collected could be biased due to the presence of spam and low-qual- ity data; measures of trust or reliability of users may alleviate this problem.