When developing a database of articles in a Knowledge Base (for example) - what are the best ways to sort and display the most relevant answers to a users' question?
Would you use additional data such as keyword weighting based on whether previous users found the article of help, or do you find a simple keyword matching algorithm to be sufficient?
Perhaps the easiest and most naive approach that will give immediately useful results would be to implement *tf-idf:
Variations of the tf–idf weighting scheme are often used by search engines as a central tool in scoring and ranking a document's relevance given a user query. tf–idf can be successfully used for stop-words filtering in various subject fields including text summarization and classification.
In a recent related question of mine here I learned of an excellent free book on this topic which you can download or read online:
An Introduction to Information Retrieval
That's a hard question, and companies like Google are pushing a lot of efforts to address this question. Have a look at Google Enterprise Search Appliance or Exalead Enterprise Search.
Then, as a personal opinion, I don't think that any "naive" approach is going to improve much the result compared to naive keyword search and ordering by the number of views on the documents.
If you have the possibility to expose your knowledge base to the web, then, just do it, and let your favorite search engine handles the search for you.
I think the angle here is not the retrieval itself... its about scoring the relevence of the information retrieved (A more reactive and passive approach) which can be later used to improve the search engine.
I guess you can try -
knn on tfidf for retrieving information
Hand tagging these retrieved info a relevency score
Then regress that score to predict the score for an unknwon search result and sort it.
Just a thought...
The third point is actually based on Rocchio algorithm. You can see it here
A little more specificity of your exact problem would be good. There are a lot of different techniques that you can use. Many of these are driven by other pieces of data. You can of course use Lucene and build your own indexes. There are bindings for many languages to lucene. Moving up there is also the Solr project which is Lucene with a lot of tools and extra functionality around it. That may be more along the lines of what you are looking for.
Intent is tricky and most modern search engines rely on statistical intent to aid in the ordering of results. You can always have an is this article useful button and store the query text that leads to useful documents. You could then add a layer of information to the index to boost specific words or phrases and help them point to certain documents.
Some things to think about...How many documents? What is the average length? Are they updated frequently? What do users do with the documents? What does the spread of unique words to documents look like? (More simply is it easy to match a query with a specific document(s) based on common unique features.)
If it is on the web you can always make a google custom search engine that just searches your site although you may find this to be sub-optimal for a variety of reasons.
You can always start with a simple index and gradually make it more sophisticated by talking with users and capturing data.
keyword matching is not enough when dealing with questions, you need to understand intent, as joannes say a very hot topic in search
Related
Does elastic search utilize the frequency of a previously searched document. For example there are document A and document B. Both have similar score in terms of edit distances and other metrics however document A is very frequently searched and B is not. Will elastic search score A better than B. If not, how to acheive this?
Elasticsearch does not change score based on previous searches in its default scoring algorithm. In fact, this is really a question about Lucene scoring, since Elasticsearch uses it for all of the actual Search logic.
I think you may be looking at this from the wrong viewpoint. Users search with a query, and Elasticsearch recommends documents. You have no way of knowing if the document it recommended was valid or not just based on the search. I think your question should really be, "How can I tune Search relevance in an intelligent way based on user data?".
Now, there are a number of ways you can achieve this, but they require you to gather user data and build the model yourself. So unfortunately, there is no easy way.
However, I would recommend taking a look at https://www.elastic.co/app-search/, which offers a managed solution with lots of custom relevant tuning which may save you lots of time depending on your use case.
I'm learning about ElasticSearch and enjoying every minute of it. However, there are some practical issues that are confusing me and of course lack of experience that I think seeing some good real life examples might clear up.
Now I am working on a website where I have accounts and products catalog and I want to search for best product matches when end-user searching for products depending on distance, relevance queries and so many criteria .
Particularly interested in:
Relevance Scoring and ranking strategies
Analyzing data of products catalog
Filtering
I would appreciate any references.
P.S
I am using Nest for .net to communicate with ElasticSearch Cluster
Well those three subjects are quite wides. A lot of works has been done on it. You should take some time to look at the elastic search documentation, for your problem, I would recommend you to have a look to the following page first:
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html (For the scoring of your document based on the distance)
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-filter-context.html (For the filtering)
Concerning your last point, the analysing part, I would recommend that you have a look to Kibana:
https://www.elastic.co/products/kibana
First, I'd recommend this article by Alexander Reelsen — Implementing A Modern E-Commerce Search. Great content on e-commerce product catalogues, filtering, and relevance in general (hint — there's no single optimal approach to achieve "relevance").
Secondly, I recently published a handbook for people just like you who need some good real-life examples — you can purchase it at https://elasticsearchbook.com. It contains concise guides on topic like faceted search, filtering, deduplication, autocomplete etc.
I have a problem with making search output more practically usefull for the end users. The problem is rather related to the algorithm and approach then to exact technology or framework to use.
At the moment we have a database of products, that can be described with following schema:
From the search perspective we've done pretty standard things, 3-rd party text search with token analyzer, handling mistypes and synonyms (it is not the full list, but as I said, it is rather out of scope). But stil we need to perform extra work to make the search result closer to real life user needs, probably, in somewhat similar way how Google ranks indexed pages by relevancy. Ideas, that we`ve already considered as potentially applicable in solving the problem:
Analyze most popular search requests in widespread search engines (it is still a question how to get them) and increase rank for those entries in the index, which correspond (could be found with) to the popular requests;
Increase rank for newest (hot) entries;
Increase rank for the biggest group of entries, which correspond to the popular request and have something in common (that`s why it is a group);
Appreciate for any help or advising a direction, where to dig.
You may try pLSA; there are many references on the web, and there should be libraries and source code.
EDIT:
well, I took a closer look at Lucene recently, and it seems to give a much better answer to what the question actually asked (it does not use pLSA). As for the integration with db, you may use Hibernate Search (although it does not seem to be as powerful as using Lucene directy is).
A new project with some interesting requirements has arrived on my desk. I need to develop a searchable directory of businesses, with a focus on delivering relevant results based on arbitrary search queries. The businesses can be of any niche; there's no one area that is more represented than another.
When googling for things like "search algorithm" or "content relevance algorithm," all I get are references to Google's "Mystical Algorithm of the Old Gods" and SEO firms.
Does the relevance value of MySQL's full text Match() function have what it takes for the task? I've never used it, but I'm definitely going to do some testing. Also, since this will largely be a human edited directory, I can assume that we can add weighted factors like tagging and categories. What would be a good way to combine these factors with MySQL's Match() relevancy?
I'm also open to ideas that I've not discussed here.
For an example of information retrieval based techniques lookup TF-IDF or BM25.
For machine learning based techniques, lookup RankNet and its variants from MSR.
If you have hand edited data, have a look at Oracle text search. In one of my previous projects we had some good results.
I was not directly involved in the database setups, but I know that the results were very welcome. (Before this they had just keyword based search).
Use a search engine like Solr to index the data. You can still use MySql to hold the data, but for searches use a search engine.
Given a few words of input, I want to have a utility that will return a diverse set of relevant terms, phrases, or concepts. A caveat is that it would need to have a large graph of terms to begin with, or else the feature would not be very useful.
For example, submitting "baseball" would return
["shortstop", "Babe Ruth", "foul ball", "steroids", ... ]
Google Sets is the best example I can find of this kind of feature, but I can't use it since they have no public API (and I wont go against their TOS). Also, single-word input doesn't garner a very diverse set of results. I'm looking for a solution that goes off on tangents.
The closest I've experimented with is using WikiPedia's API to search Categories and Backlinks, but there's no way to directly sort those results by "relevance" or "popularity". Without that, the suggestion list is massive and all over the place, which is not immediately useful and very hard to whittle down.
Using A Thesaurus could also work minimally, but that would leave out any proper nouns or tangentially relevant terms (like any of the results listed above).
I would happily reuse an open service, if one exists, but I haven't found anything sufficient.
I'm looking for either a way to implement this either in-house with a decently-populated starting set, or reuse a free service that offers this.
Have a solution? Thanks ahead of time!
UPDATE: Thank you for the incredibly dense & informative answers. I'll choose a winning answer in 6 to 12 months, when I'll hopefully understand what you've all suggested =)
You might be interested in WordNet. It takes a bit of linguistic knowledge to understand the API, but basically the system is a database of meaning-based links between English words, which is more or less what you're searching for. I'm sure I can dig up more information if you want it.
Peter Norvig (director of research at Google) spoke about how they do this at Google (specifically mentioning Google Sets) in a Facebook Tech Talk. The idea is that a relatively simple algorithm on a huge dataset (e.g. the entire web) is much better than a complicated algorithm on a small data set.
You could look at Google's n-gram collection as a starting point. You'd start to see what concepts are grouped together. Norvig hinted that internally Google has up to 7-grams for use in things like Google Translate.
If you're more ambitious, you could download all of Wikipedia's articles in the language you desire and create your own n-gram database.
The problem is even more complicated if you just have a single word; check out this recent thesis for more details on word sense disambiguation.
It's not an easy problem, but it is useful as you mentioned. In the end, I think you'll find that a really successful implementation will have a relatively simple algorithm and a whole lot of data.
Take a look at the following two papers:
Clustering User Queries of a Search Engine [pdf]
Topic Detection by Clustering Keywords [pdf]
Here is my attempt at a very simplified explanation:
If we have a database of past user queries, we can define a similarity function between two queries. For example: number of words in common. Now for each query in our database, we compute its similarity with each other query, and remember the k most similar queries. The non-overlapping words from these can be returned as "related terms".
We can also take this approach with a database of documents containing information users might be searching for. We can define the similarity between two search terms as the number of documents containing both divided by the number of documents containing either. To decide which terms to test, we can scan the documents and throw out words that are either too common ('and', 'the', etc.) or that are too obscure.
If our data permits, then we could see which queries led users to choosing which results, instead of comparing documents by content. For example if we had data that showed us that users searching for "Celtics" and "Lakers" both ended up clicking on espn.com, then we could call these related terms.
If you're starting from scratch with no data about past user queries, then you can try Wikipedia, or the Bag of Words dataset as a database of documents. If you are looking for a database of user search terms and results, and if you are feeling adventurous, then you can take a look at the AOL Search Data.