Whitepaper – Mobile, Social Search: A Case Study

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Free Whitepaper - Mobile Social Search: A case study

By Prof. Barry Smyth, HeyStaks’ Chief Scientist

 

We are living in a discovery economy where access to the right information at the right time can make the difference between success and failure. The ability to use modern information discovery tools such as search engines, social media, and related services is an important skill for us all to master, particularly on mobile devices where new tradeoffs exist when it comes to searching for and finding information online. In this paper we describe one such solution in the form of collaborative search, which combines conventional term-based search with a more social approach to information discovery that is particularly well-adapted to the constraints of mobile devices.

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The Selfish Web: Is Sharing Really Caring?

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In an earlier post, we described how the term “social search” can be roughly translated to mean “searching the social Web” for many of the offerings out there. We argued that social search can be thought of as a process, not just another source of results. Also, the differing intents behind sharing on social networks and searching may result in lower relevance when using social posts as search result recommendations. Here we will discuss how the social Web – although undoubtedly a useful source of relevant results for searching – may not cover the gamut of peoples’ searching interests and that the motivations behind posts on social media sites may limit their usefulness as a reliable source of search recommendations.

Sharing is only partly caring

The psychology of sharing has been well studied in recent times, with many motivations identified as to why people choose to post things to social media. Altruism, self-promotion, validation, relationship-building; all feature across various studies, as does building thought leadership or authority, image- or brand-construction. Even altruism itself can be viewed as self-serving, though studies have shown that humans may possess an “altruism instinct“.

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Public vs Private Parts: Personas & Sharing on the Recommendation Web

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The Internet is an archive of our lives. Every event captured, every photo rendered, and every conversation indexed. And, if it can be stored it can be found. Maybe not today but someday, by a friend perhaps, or a future employer, whether we want it to be found or not. This is both liberating and terrifying. But is it creating an incentive for people to hide their true personalities? Are we curating carefully crafted personas online that only disguise our genuine personalities? If so then, what we share may not be what we click and this has some important implications for the future of personalization, sharing, and recommendation on the web.

Figure 1. An analysis of what people share versus what is clicked, by 33Across, and based on 450 large publishers and 24 content categories.

Figure 1. An analysis of what people share versus what is clicked, by 33Across, and based on 450 large publishers and 24 content categories.

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Whitepaper – Social Search and Search Analytics in the Discovery Economy: An Enterprise Perspective

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By Prof. Barry Smyth, HeyStaks' Chief Scientist

By Prof. Barry Smyth, HeyStaks’ Chief Scientist

Knowledge workers continue to struggle when it comes to finding the right information at the right time, leading to high search failure and abandonment rates – 50% of queries lead to failed searches and 44% of knowledge-workers fail to find what they are looking for – a significant cost to enterprise in terms of lost productivity and missed opportunities. One practical solution is for a more collaborative approach to search, which harnesses the past search patterns of experts within an organisation, and works in tandem with conventional search services to provide more relevant and useful results. By harnessing the power of collaborative search, HeyStaks can improve search effectiveness within the enterprise by up to 50% and by fostering improved collaboration HeyStaks will improve engagement, knowledge sharing, and innovation right across an enterprise.

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Social search: searching the social Web or a social form of Web search?

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For a number of years now, a growing trend in online information retrieval has been “social Web search” (or simply “social search”). It’s a term that continues to be tweaked and defined, and there are some quite different interpretations of what social search actually means. Generally speaking folks interpret social search to mean a form of Web search informed by the searcher’s social graph. The way in which the social graph informs search tends to be searching social feeds from the likes of Facebook, Google Plus and – once upon a time – Twitter. Posts or status updates containing links that match the user’s query can be integrated with regular search results. In this post we explore some of the ramifications of this approach to social search and suggest a different way of looking at things.

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The Evolution of Web Search:From Real-Time Discovery to Collaborative Web Search

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Certainly the world of the Web has changed dramatically since 2000, and search engine technology has evolved through a variety of phases. For example, in the pre-Google dawn (Search 1.0), search engines were guided primarily by the words in a page, their location and how they matched the query terms. Google’s great innovation was to demonstrate how search quality could be greatly enhanced by harnessing a new relevance signal: the links between pages. Google’s link analysis technology (PageRank) interpreted links to a page as votes and PageRank was a clever way of counting such votes to effectively compute an authority score for each page, which could then be used during result ranking.As an aside, back in the late 1990’s one of Google’s fellow innovators was a company called Direct Hit, which also argued for the need for new relevance signals. But in the case of Direct Hit the focus was on paying attention to how often users selected a page for a given query, something we will return to later. In the end Google’s PageRank was the right search technology at the right time and the rest, as they say, is history. And so Search 2.0 was primarily driven by relevance signals (links, click-thrus) that originated beyond the content of a page. More recently we have seen further innovation in the direction of vertical search (arguably Search 3.0) for topics such as images, travel, products etc. and the blending of different types of result within a universal search interface (see for example, Google’s Universal Search.

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Harnessing the Latent Knowledge in Your Organization

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The field of knowledge management is concerned with making it easier for insights and experiences to be shared within organizations. Despite the importance of knowledge management in today’s information-centric economy, there is a huge amount of latent knowledge that still goes untapped within most organizations. However, this latent knowledge has the potential to drive innovation and to greatly improve information access within the organization. And, importantly, the ability to realize this potential is readily available.

What is Latent Knowledge?

So what exactly do I mean by latent knowledge? The field of knowledge management generally differentiates between explicit knowledge and tacit knowledge. Explicit knowledge is easy to record and make available to others who can then learn from it. Tacit knowledge, however, is harder to pin down and is the sort of know-how that is better transferred directly from person to person, for example through apprenticeships. More recently, knowledge management experts have also started to talk about latent knowledge. Latent knowledge can be thought of as the building blocks of knowledge creation – it may not have coalesced yet into tacit or explicit knowledge, but individuals possess elements of it. And through group collaboration this latent knowledge can be surfaced to produce new ideas and innovations; aka, knowledge creation.

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What You Can Learn by Analysing Your Customers’ Search Data

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The HeyStaks Discovery Analytics product provides and interface to visualise the results of big data analysis

On-site search is an important navigational tool for ecommerce and content-based websites. When a large amount of content exists on a site, complex navigation can be inescapable, making it difficult for users to find their way around the site. On-site search is frequently chosen as the best option to help simplify user experience.

But what can we learn from the data that comes from your users’ on-site searches? What kind of insights does this search data provide, and how can you use this information to increase engagement and conversions?

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Context-defined Communities & Personalization

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Our short- and long-term context defines who we are and what we’re interested in. Furthermore, when we can automatically identify others who have a similar contextual makeup and group them together, a powerful form of personalized recommendation is possible as we search and browse. As we go about their daily lives, our context changes. While engaging in projects at work, taking up new hobbies, attending events, using apps and searching on particular topics, these activities reflect our interest profiles. Some interests are long-lived, such as an interest in science, a career in a particular field or an artistic hobby. Others are more short-lived, such as attending an art exhibition, listening to a talk at a conference, frequenting a store or executing a Web search. Short-term interests can often be connected to long-term interests (e.g. listening to a talk at a conference is usually linked to a long-term interest such as a career), and in general we can characterize both short- and long-term interests as elements of a person’s contextual makeup.

 

HeyStaks Keynote Example

 

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