182 D MCSHERRY in that critiques are dynamically generated by the system and may involve compromises as well as improvements relative to the currently recommended case. In this way, the user is informed in advance of trade-offs associated with desired improvements. Before selecting a suggested critique, the user can ask to see an explanation of the trade offs involved In recommender systems that treat some or all of the user's require ments as constraints that must be satisfied, explanation can also play an important role in recovery from the retrieval failures that occur when there is no exact match for the user's requirements(Hammond et al., 1996; McSherry, 2004). Hammond et al.s(1996)Car Naviga tor is a recommender system for cars that uses declarative knowl edge to explain trade-offs that are known to be common causes of retrieval failure in the domain, such as that between fuel economy and horsepower. For example, if the user asks for good fuel economy and high horsepower, she is shown a movie explaining the trade-off between these features. The user is also advised that she will need to revise her query if she hopes to find a car that meets her require ments In recent work, we combined a knowledge-light approach to expla nation of retrieval failure with a mixed-initiative approach to recovery from retrieval failure in a CBR recommender system called Show Me (McSherry, 2004). Failure to retrieve a case that exactly matches the user's query triggers an explanation that draws the user's attention t combinations of features in her query for which there are no matching cases e orry, there are no products that match these combinations of fea- tures in your query:(price 500, type laptop),(type laptop screen size = 19) As well as highlighting areas of the product space in which the case library is lacking in coverage, the explanation may reveal misconceptions on the part of the user such as the price she expects to pay for the prod uct she is seeking. Showing the user only the minimally failing sub-queries of her query, a technique we have adapted from research on co-opera- tive responses to failing data base queries( Gasterland et al., 1992), helps to minimise cognitive load in the approach. Explanation of the retrieval failure is followed in Show Me by a mixed-initiative recovery process in which the user is guided in the selection of one or more constraints to be eliminated from her query(Mcsherry, 2004)182 D. MCSHERRY in that critiques are dynamically generated by the system and may involve compromises as well as improvements relative to the currently recommended case. In this way, the user is informed in advance of trade-offs associated with desired improvements. Before selecting a suggested critique, the user can ask to see an explanation of the trade￾offs involved. In recommender systems that treat some or all of the user’s require￾ments as constraints that must be satisfied, explanation can also play an important role in recovery from the retrieval failures that occur when there is no exact match for the user’s requirements (Hammond et al., 1996; McSherry, 2004). Hammond et al.’s (1996) Car Naviga￾tor is a recommender system for cars that uses declarative knowl￾edge to explain trade-offs that are known to be common causes of retrieval failure in the domain, such as that between fuel economy and horsepower. For example, if the user asks for good fuel economy and high horsepower, she is shown a movie explaining the trade-off between these features. The user is also advised that she will need to revise her query if she hopes to find a car that meets her require￾ments. In recent work, we combined a knowledge-light approach to expla￾nation of retrieval failure with a mixed-initiative approach to recovery from retrieval failure in a CBR recommender system called ShowMe (McSherry, 2004). Failure to retrieve a case that exactly matches the user’s query triggers an explanation that draws the user’s attention to combinations of features in her query for which there are no matching cases e.g. Sorry, there are no products that match these combinations of fea￾tures in your query: (price ≤ 500, type = laptop), (type = laptop, screen size = 19) As well as highlighting areas of the product space in which the case library is lacking in coverage, the explanation may reveal misconceptions on the part of the user such as the price she expects to pay for the prod￾uct she is seeking. Showing the user only the minimally failing sub-queries of her query, a technique we have adapted from research on co-opera￾tive responses to failing database queries (Gasterland et al., 1992), helps to minimise cognitive load in the approach. Explanation of the retrieval failure is followed in ShowMe by a mixed-initiative recovery process in which the user is guided in the selection of one or more constraints to be eliminated from her query (McSherry, 2004)