Here, we propose a topic sensitive hybrid expertise retrieval system in community question answering services. We introduce three new expertise signatures: knowledge, reputation, and authority. These signatures consider the questions, and hence, their answerers from a topic sensitive perspective. We estimate the knowledge of an answerer on a new question based on the previously answered subset of questions with similar topic distributions to the new question. The reputation of an answerer, moreover, is derived from the qualities of previously answered questions by the answerer with similar distributions of topics. Furthermore, we propose a topic sensitive authority model. It considers some topic related information associated with questions and the relationships among their answerers. We compare the proposed method with 26 existing methods on 4 real-world datasets using 5 performance measures. It outperforms the comparing algorithms in 91.73% (477 out of 520) cases.

This paper introduces a time-aware hybrid expertise retrieval (TaHER) system for community question answering (CQA) services. It comprises of a text-based part and a network-based part. The text-based part makes use of the textual and the temporal information associated with questions and answers. Moreover, it assesses the recent interests and the activities of answerers. For a given question, it determines the knowledge of each answerer and identify active answerers with adequate knowledge. The network-based part is composed of several period-dependent networks. It uses the relationships among the answerers along with temporal information. Next, it applies a link analysis technique on the networks to determine the time-aware authority of each answerer in the community. We, nonetheless, propose a fusion strategy for combining the offshoots of these two parts. Using 5 performance measures, TaHER system is compared with 20 state-of-the-art algorithms on 4 real-world datasets. According to our experiments, in 93.75% (375 out of 400) cases, the proposed approach outperforms the comparing approaches. We also experimentally validate the importance of each assumption used by us.

Here, we propose a preference enhanced hybrid expertise retrieval (PEHER) system in community question answering services. PEHER consists of three segments, namely, preferability estimator, authority estimator, and expertise estimator. The preferability estimator utilizes the textual information to determine both intra-profile and inter-profile preferences of answerers for each term. The intra-profile preferences consider the preference of a term using the answering history of a given answerer. The inter-profile preferences incorporate the preferences of all answerers for a term. These preferences are then used to determine the preferability of each answerer for each of the archived questions. The authority estimator considers the textual familiarity between each archived question and the profile of each answerer as the weight of the associated link in the network. The expertise estimator is composed of three blocks, namely, question similarity finder, proficiency estimator, and expert list generator. The question similarity finder finds the similarities between the new question and each of the archived questions. The proficiency estimator uses the said similarities of the archived questions along with their preferabilities to decide the proficiencies of answerers for the new question. Finally, the expert list generator considers the authorities and proficiencies to generate a list of experts for a given question. We compare PEHER with twenty existing methods on four real-world datasets using five performance measures. We find that PEHER outperforms the comparing algorithms in 92.00% (368 out of 400) cases.

In this article, we propose a method for finding active experts for a new question in order to improve the effectiveness of a question routing process. By active expert for a given question, we mean those experts who are active during the time of its posting. The proposed method uses the query likelihood language model, and two new measures, activeness and answering intensity. We compare the performance of the proposed method with its baseline query likelihood language model. We use a real-world dataset, called History, downloaded from Yahoo! Answers web portal for this purpose. In every comparing scenario, the proposed method is found to outperform the corresponding baseline model.

In this paper, we propose a hybrid expertise retrieval system for community question answering services. The proposed system consists of two segments: a text based segment and a network based segment. For a given question, the text based segment estimates users’ knowledge introducing two new concepts: question hardness and question answerer association. The network based segment, moreover, incorporates users’ relative performances into the network structure. We denote the outputs of these two segments as knowledge score and authority score, respectively. We aggregate these two scores using a fusion technique to quantify the expertise of a given user for a given question. We have generated four datasets by downloading questions and answers from Yahoo! Answers. The performance of the proposed system is found to be superior than that of 18 state-of-the-art algorithms on these four real-world datasets.

Community question answering services provide an open platform for users to acquire and share their knowledge. In the last decade, popularity of such services has increased noticeably. Large number of unanswered questions is a major problem for the growth of such services. A common way to address this issue is to route a new question to some selected users who have the potentiality in answering the question. Expert finding is the process of selecting such potential answerers. In this article, we have introduced an efficient method for expert finding using the theme in query likelihood language (QLL) model. Theme of a query is nothing but its subject matter and we have decided it based on the parts of speech (POS) of the words in the query. Depending on the theme of the given question, its similarity to a question in the archive is determined using the QLL model. Aggregating the similarity values of the questions a user answered previously (i.e., in the archive), his/her expertise for the given question is obtained. The performance of the proposed method is verified on a real world dataset (obtained from Yahoo! Answers) and it is found to be quite encouraging.