Using Link Management to Help Power Suggestions in Java

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Using Link Management to Help Power Suggestions
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So, after you've developed this elegant database schema around your data, what else can you do with it Remember when I was spouting off about "emergent properties" of systems in the Introduction A database schema based on the idea of partial decomposition can also exhibit emergent properties, developing smarts beyond the original design A good example of this is in the field of suggestions The complex maps you've built in the review_movie, review_review, and review_person tables can be even more useful, especially if you're interested in making suggestions to users, that is, providing information they might be interested in based on what they're looking at now The science of suggesting relevant information is nothing new Many software systems do this through methods such as collaborative filtering or lexical analysis The idea behind lexical analysis systems is to break documents down to their lexical components ords or phrases nd compare new documents based on a statistical relevance ranking of these components In theory, documents that have similar subject matter should rank closely together; in reality, lexical analysis often groups documents that don't necessarily go together For instance, lexical engines can readily group all of Shakespeare's works together, but a lexical system can't differentiate between a comedy and a tragedy If you were reading Troilus and Cressida and you wanted the system to show you "more plays like this one," it probably could manage to rule out Speed the Plow, but you might just as easily be shown As You Like It as Romeo and Juliet The system needs more information than purely lexical similarity to guide it Likewise, collaborative filtering, when left to its own devices, can develop strange behaviors Collaborative filtering attempts to suggest items to you based on matching your own likes and dislikes against all other users on the system Collaborative filtering's answer to the previous user request involves a complex equation based on what other readers who had read Troilus and Cressida had read and enjoyed This means that you might get a list of other Shakespearean tragedies as well
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as Greek tragedies and other similar fare Collaborative filtering misses any advantage it could gain from the content itself, however You might as well be reading Dick and Jane r eating avocados for that matter Collaborative filtering system knows only about user responses Both lexical analysis and collaborative filtering lack a means to receive editorial input into the suggestion process If, in the editorial notes for Troilus and Cressida, a discussion of Shakespeare's possible influences for this work and his similar works is included, and this discussion includes links to those other works, we now have something to work with Instead of having to infer the relationships between documents by essentially throwing a dart at a board, the system has hard data about these relationships It can use lexical analysis or collaborative filtering (or both) to supplement this hard data and give the user access to a richer experience In reality, especially in systems with lots of legacy information, hard data about the relationships between documents isn't always possible to get, but when this data is available, you should leverage it nd hard If your database schema is built correctly and you've used the tools of link management and partial decomposition, you already have a mechanism to manage these types of relationships between documents and to use them to best effect Your database now knows more about the relationships between your documents than you do because it knows all of the links between documents or from documents to other entities (movies, actors, directors, and so on) However, you have to manage the data input process very closely; otherwise, as they say in the biz, "garbage in, garbage out" will rule the day The key is in correctly building the input tools for your internal user base For example, at CyberCinema, it might be useful to have two kinds of links between articles: One kind might be "related reviews based on topic" (such as "Academy Award nominees for 2001"), and the other might be "related reviews, based on genre" (such as "science fiction" or "Chinese films") In both cases, you're relating one film review to another but for different reasons You want to be able to store those links, but also you also want to be able to store the accompanying data of why those links exist What's the semantic value of that link An effort is currently underway within the hallowed halls of the World Wide Web Consortium (W3C) to build an XML-based language to address just this problem The language is called RDF (Resource Description Framework), and its stated goal is to build a new "semantic Web," a Web composed of intelligent links that encode their own raison d' tre It's a splendid goal, but reaching it probably is way in the future To read more about RDF, visit W3C's RDF pages at http://wwww3org/RDF/ Tim Bray has written an excellent primer at http://wwwxmlcom/pub/a/98/06/rdfhtml
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In the meantime, you can build your own semantically encoded links using the tools of XLink and partial decomposition First, let's take our XLink example from 5 and add optional attributes:
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<!ELEMENT review ANY> <!ATTLIST review xlink:type (simple|extended|locator|arc) #FIXED "locator" <!-- This is a locator link because it points to an external resource --> xlink:href xlink:type xlink:show the linked-to data come up in a new window, be embedded in the current window or replace the current content --> xlink:actuate request (for example, the user clicks on the link text) --> >
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Now let's imagine the following partial decomposition table in which to store this link data:
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CDATA (topic | genre | other) (new | embed | replace)
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#REQUIRED "other" "replace"
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