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Chapter 2 LITERATURE REVIEW
In this chapter, we will organize recommendation mechanisms in the healthcare domain and recommendations in the asking process.
2.1 Recommendation mechanisms in healthcare domain
Healthcare services have animated in health information systems. In this context, the recommendation mechanism is a complementary tool when users are making decision or a service provider uses to improve the effectiveness of communication channels. David Isern et al. (2016) organized various agents applied in healthcare to support determinations. For example, intelligent agents are able to give cure plans when patients are facing specific situations and patient-centered applications are created to alert patients when abnormal messages are detected. Other researchers demonstrated recommendation system has already employed in health areas like education, dietary, assistance agents in different classes (collaborative, content-based, demographic, and knowledge-based) of recommendation techniques based on knowledge source (Kim, Lee, Park, Lee, & Rim, 2009; Pattaraintakorn, Zaverucha, & Cercone, 2007; Sami, Nagatomi, Terabe, & Hashimoto, 2008; Wiesner & Pfeifer, 2010). Emre Sezgin et al. (2013) summarized papers related to health recommender systems are usually analyzed by users’ group and system design (Pattaraintakorn et al., 2007; Wiesner
& Pfeifer, 2010). Besides, electronic records on healthcare websites are another focal point when doing health marketing, recommendations based on personal information and self-examination (Lopez-Nores, Blanco-Fern´ndez, Pazos-Arias, Garcia-Duque, & Martin-Vicente, 2011; Pattaraintakorn et al., 2007; Wiesner & Pfeifer, 2010). Though Morrell et al. (2012) underlined semantics on webs is a challenging task for predicting user behaviors, researchers still tried content-based filters (examining the historical data and current preference of users to predict items) to attract one’s attention from designed algorithms (Kim et al., 2009; Park, Kim, Choi, & Kim, 2012; Sami et al., 2008).
Unlike the recommendation mechanism before and after the post action, our research put more efforts in the searching and posting step. We roughly observe recommendation patterns of whole asking procedure (e.g. start to type, check the recommendation, and decide to use) and found recommendation mechanisms in health forums are similar to general topics forums.
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Users type keywords like symptoms or conditions into blank searching box and the recommender will pop out relative illnesses’ wordings or asked sentences containing identical words (Patient3 and WebMD4). Specially, most online healthcare communities tend to combine the characteristic of information conveyers (e.g. medical news and effective treatments) with platform providers (e.g. offer places where askers and experts can share their experiences). It is a feasible way to keep member engaged because the more useful information a forum has, the more users will adopt solutions and stay. If more users stay and activate, the connection between users become tight, so the community is able to survive longer (Williams, R. L., &
Cothrel, 2000). In addition, different from only browsing and searching messages as visitors, being a forum member can get more assistances sometimes because predictions based on behavior’s data may satisfy each user’s need. It is obvious that most recommender agents or systems contained in the asking procedure follows the tradition to guess intentions and give suggestions related to what a user has typed.
2.2 Recommendation in the asking process
Most recommendations during the posting step of whole asking process adopt various analysis from the exist Q&A database to help new askers to solve their problem faster. Question routing and recommendation studies before (Dror, Koren, Maarek, & Szpektor, 2010; Li & King, 2010;
Riahi, Zolaktaf, Shafiei, & Milios, 2012) have explored methods to find potential answers and answerers (people who have similar experiences in a thing) in Q&A forums. They consider underlying social network features (e.g. which query get more than x times hits), users’
activities (e.g. which category that an expert likes to reply and always get the honour of best answer) and public personal data on websites to improve systems’ usability. Several methodology were developed to make systems efficient for users: words’ collections by grouping questions from two similar answers (Jeon, Croft, & Lee, 2005), finding different relationships between specific data (Zhao, Collins, Chevalier, & Balakrishnan, 2013), and adding neural models into applications (Feng, Xiang, Glass, Wang, & Zhou, 2016; Shen, Rong, Sun, Ouyang, & Xiong, 2015; Zhou, He, Zhao, & Hu, 2015). Question-question similarity and answer selection are both common recommendations seen in a real forum.
3 https://patient.info/forums
4 https://www.webmd.com/
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Refer to the Quora forum, we found most of the questions on general forums (various topics include food, education, country recognition, and etc.) start from 5W1H and short questions usually get answers faster than the longer one. Perhaps it is because Quora alludes querents to ask in a specific structure and provide limited space to control writing short. The number of short questions then grows up. After finishing typing the post, askers can invite answerers who specialize in the corresponding category to reply to their questions. Even if askers do not send any invitations, their posts will be spread to the public and showed to users who are interested in. Operations on Quora such as real name policy, answer recommendations, content moderation, and top writer program are components we can mimic when developing a Q&A-related system.
However, universal situations aren’t always suitable for the healthcare environment. Yan Zhang (2010) stated that when askers are posting questions in a health-related forum, they prefer more spaces letting them write as much as details they want to share. Many askers valued personal experiences and sought actively for someone they can talk to. Further, some askers have difficulty with spellings and come up with proper terms to describe their condition. So, this paper focuses on developing a model to suggest potential ideas, formulate users’ questions and avoid using ambiguous terms that decrease answered possibility and increase the length of answered time. At the beginning of proposing the question, users usually have a strong intention to get a beneficial reply and motivate them to accept or follow the instruction easily.
Therefore, even though there is vindication of using short guidance to users (Quora’s 5W1H and limited space), we decide to recommend users relative features during query’s construction and most importantly our research stresses on health care domain which has obvious differences to general forums. Questions like “having severe lower back stiffness/pain. can you take cele atbrex with coumadin” and “pain on both side of chest” seen on WebMD answer (look far away from a standard formulation) cause difficulties to background programs in categorizing questions by analyzing wordings. Specific-domain questions do need a descriptive of the situation sometimes so our goal is to develop an effective solution to formulate UGCs.