Reputation Inc. – The Value of Social Reputation on the Recommendation Web

Way back in the pre-Google dawn of the 1990’s Internet there was a much heralded approach to web search by a company called DirectHit. The message was simple: paying attention to the words in a document (and query) was not enough to do a good search job, we need to pay attention to the results people select.

To be fair, the first part of this idea – that the words or terms in a query and document were not enough – was accepted by then; at the time a couple of grad students at Stanford were doing some interesting things with links as a result ranking signal for the same reason. But where Boston-based DirectHit differed was it’s emphasis on engagement signals. For instance. the Direct Hit search engine harnessed the searching activity of millions of anonymous web searchers to rank websites based on often searchers selected a page, how long searchers spent viewing it, and where the page was ranked in the original search results list. Ultimately, Direct Hit’s so-called Popularity Engine ranked search results based on a formula that combined a variety of engagement signals to evaluate the page’s popularity. At the time the idea was fascinating and potentially powerful; so much so that Direct Hit was acquired by Ask Jeeves for more than $500m in stock.

View full post


Whitepaper – Mobile, Social Search: A Case Study

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.

View full post


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

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.

View full post


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

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.

View full post


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

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.

View full post


Harnessing the Latent Knowledge in Your Organization

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.

View full post