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

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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.

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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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